Conference Proceedings

The population of urban area is continuously increasing nowadays. It tends to decrease the public spaces in highly populated area. There are many spaces in urban area which can be converted into different uses like urban voids i.e. leftover spaces, negative spaces etc. have huge potentials to turn the spaces into well planned, designed most effective public spaces. Filling the urban voids such as spaces below flyover gives city a new look and life, it can be possible to convert leftover spaces like edge spaces, in between spaces, below flyover spaces etc. into spaces that are beneficial to public and community. The paper will be focused on the concept of urban voids, identifying and analyzing the types of voids also filling these voids in order to create healthy, enjoyable public spaces by studying the case studies of western and European countries.

Keywords : Urban Voids, Spaces below Flyover, Left Over Spaces Utilization, Public Spaces

Recent

[1] Ms. Bhaskaran Rekha, Urban void- a “Bypass” urban resource, New Delhi [2] France Ari Prasetyo, August 2014, Playing Under The Flyover: A Collaborative Creative Community in Bandung, [3] Md Azree Othuman, Nangkula Utaberta, Imrpovise Overpasses: Study on utilizing space below flyover, IJAES ISSN 0973-4562, volume 10, 2015 [4] Nurulhusna Qamaruz-Zaman, Zalina Samadi, Under the flyovers of Kuala Lumpur, Malaysia, journal of Asian Behavioural Studies, volume 3, number 10, 2013. [5] Shukla Dipti, 15th September 2016, “Flyovers”- infrastructural barrier or potential opportunity for knitting city fabric. [6] Seog Jeong Lee, Soewon Hwang and Dongha Lee, Urban Voids: As a Chance for Sustainable urban design, Korea. [7] Trancik Roger, 1986, Finding Lost Space: Theories of Urban Design, New York. [8] http://www.wanurbanchallenge.com/award/sector/rec laiming-the-streets-18 [9] http://www.suratmuniipalcorporation.com

Improvement of potholes on streets and roads of India after the onset of storms is a typical marvel. Frequently, potholes are repaired without dated methods, for example, putting soil or exposed aggregate in the pothole in light of the fact that no hot mix asphalt is accessible amid storms and winter season. In today's situation street condition are seeing to be better because of joining of interchange strategies, procedures, innovations. An economical, nonexclusive, readymade stockpile cold patching mix has been proposed, which is produced in a batch type cold mix plant utilizing local aggregate. This nonspecific mix can be put without setting up the pothole, for example, drying, squaring the edges, cleaning, and tack coating. To the motoring public, potholes are one of the most visible and annoying forms of pavement distress. Most pothole repairs made during the winter months are short-lived. Potholes that must be filled repeatedly are expensive to repair. Recent field trials of this mix in Surat City have been profoundly fruitful.

Keywords : Patch Repair Mix, Potholes Repair, Cold Patch

Recent

[1] Kandhal P.S. (2008), “A simple and Effective Method of Repairing Potholes in India”, IRC Journal. [2] Anderson, D.A., et al. 1988. More Effective Cold, Wet Weather Patching Materials for Asphalt Pavements, Federal Highway Administration, Report No. FHWARD-88-001. [3] Carpenter, S.H. and T.P. Wilson. 1991. Evaluations of Improved Cold-Mix Binders— Field Operations Plan, Federal Highway Administration, Contract No. DTFH61-90-00021. [4] Evans, L.D., et al. 1993. Materials and Procedures for Pavement Repairs—Final Report, National Research Council, Strategic Highway Research Program, Contract SHRP-89-H-106. [5] Arnold J. Hoiberg, Bituminous materials Volume II, John Wiley & sons Inc., New York 1965 [6] Robert F L. 1996. Hot mix Bitumen Materials, Mixture Design and Construction. National Bitumen Pavement Association. 2nd Edition.

Due to lack of pedestrian walking and crossing facilities pedestrian mostly used regular traffic lane. Continuously increase in motor vehicles increases chances of collision with pedestrians. In such scenario, pedestrian safety is an important issue in most of the developing countries. In developing countries like India. Pedestrian behaviour is prime important in order to provide better safety to the most vulnerable road users and to arrest illegal crossing of pedestrians and reduce crash at such location in urban area. User’s perception is very much powerful tool to carry out such behavioural study. The present study has been carried out with the objectives to analysed pedestrian safety at undesignated midblock section using user’s perception. Pedestrian’s perception information will be collected different high-density urban area with help of Questionnaire form survey. Collected Pedestrian different characteristics data such as Socio-economical characteristics data, Crossing characteristic data, Behaviour characteristic data, Traffic Characteristics data. Also ask to pedestrian regarding safety and risk. To collected pedestrian perception behaviour information through Questionnaire survey. Analysed pedestrian safety at undesignated midblock section at five scale rating. Survey is conducted at Ahmedabad; India and 78 sample of questionnaire survey is collected. Analysis of data was further analysed to assess safety perspective of pedestrian. Based on analysis results people having habits to cross at undesignated midblock section. Education and purpose not affected the safety.

Keywords : Pedestrian, User’s Perception, Safety, Midblock

Recent

[1] B Raghuram Kadali and P Vedagiri (2013). “Modelling pedestrian road crossing behaviour under mixed traffic condition”. European Transport \ Trasporti Europei. Volume 4, No 3, 2014. [2] Carol Holland, Roslyn Hill. (2017). “The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations”. Accident Analysis and Prevention. 39 (2007) 224–237. [3] Catriona Havarda and Alexandra Willis. (2012). “Effects of installing a marked crosswalk on road crossing behaviour and perceptions of the environment”. Transportation research, Transportation research part F 15,249-260. [4] Eleonora Papadimitriou, Athanasios Theofilatos, George Yannis (2012). “An Analysing the perceptions of pedestrians and drivers to shared space” Transportation research, Transportation research part F 15,297-310. [5] Fauzul Rizal Sutiknoa, Surjonoa, and Eddi Basuki Kurniawana. (2013). “Walkability and pedestrian perceptions in Malang City emerging business corridor”. Procedia environmental science, volume 17, 424-433. [6] Hongwei Guo, Facheng Zhao, Wuhong Wang, Yanlong Zhou, Yujie Zhang, and Geert Wets. (2014). “Modeling the Perceptions and Preferences of Pedestrians on Crossing Facilities” Hindawi Publishing Corporation Discrete Dynamics in Nature and Society, Volume 2014, Article ID 949475, page 8. [7] Ioannis Kaparias, Michael G.H. Bell, Ashkan Miri, Carol Chan. (2012). “Analysing the perceptions of pedestrians and drivers to shared space”. Transportation Research. Transportation Research Part F 15, 297–310. [8] Khaled Shaaban, Deepti Muley, Abdulla Mohammed (2018), “Analysis of illegal pedestrian crossing behaviour on a major divided arterial road”. Transportation research. Transportation research part F 54, 124-137. [9] National crime research bureau ministry of home affairs (NCRB), 2016.Accidental Deaths and Suicide in India. [10] Papadimitriou a, Sylvain Lassarreb, George Yannisa “Introducing human factors in pedestrian crossing behaviour models”. Transportation research, Transportation Research part F 36,69-82. [11] Pengyun Zhao, Guangquan Lu2, and Liming Liang, (2018). “Perception and Response Characteristics of Pedestrian-Vehicle Traffic Conflict at Unsignalized Intersections under Driving Distraction”. CICTP 2018 ASCE. [12] Peña-García, A. Hurtado, M.C. Aguilar, (2015). “Impact of public lighting on pedestrians’ perception of safety and well-being”. Safety science, 78, 142-148. [13] Road accidents in India- 2015, Ministry of Road Transport and Highways, Government of India, 2016. [14] Serag M.S(2014) “Modelling pedestrian road crossing at uncontrolled mid-block locations in developing countries”, International Journal of Civil and Structural Engineering. Volume 4, no 3. [15] Shalini Rankavata, Geetam Tiwari. (2016). “Pedestrians risk perception of traffic crash and built environment features – Delhi, India”. Safety science. 87, 1-7. [16] Tova Rosenbloom, Roi mandel, Yotam Rosner, Ehud Eldror. (2015). “Hazard perception test for pedestrian”. Accident Analysis and prevention.79, 160-169.

QGIS (previously known as Quantum GIS) is a free and open-source cross platform desktop geographic information system (GIS) application that supports viewing, editing, and analysis of geospatial data. Optimum route is introduced for optimized routing identification for minimization of the time as well as the fuel consumption. This is possible with the help of QGIS. Here, for optimum length was identified between two locations of the Surat city.

Keywords : Optimum Route Identification, QGIS

Recent

[1] Abousaeidi, M., Fauzi, R., & Muhamad, R. (2016). Geographic Information System (GIS) modeling approach to determine the fastest delivery routes. Saudi Journal of Biological Sciences, 23(5), 555-564. doi:10.1016/j.sjbs.2015.06.004 [2] Getting Started With QGIS @VGSOM. (n.d.). Retrieved from http://gettingstartedwithqgis.blogspot.in/ [3] Kinobe, J., Bosona, T., Gebresenbet, G., Niwagaba, C., & Vinnerås, B. (2015). Optimization of waste collection and disposal in Kampala city. Habitat International, 49, 126-137. doi:10.1016/j.habitatint.2015.05.025 [4] M, SureshKumar. (2017). GIS Based Route Optimization for Effective Traffic Management. International Journal of Engineering Research And Management (IJERM),4(3),62-65. [5] Optimal route analysis using GIS. (2016, May 03). Retrieved from https://www.geospatialworld.net/article/optimal-route-analysis-using-gis/ [6] QGIS Training Manual. (n.d.). Retrieved from http://docs.qgis.org/2.18/en/docs/training_manual [7] QGIS User Guide. (n.d.). Retrieved from http://docs.qgis.org/2.18/en/docs/user_manual [8] SinDizzysinDizzy 245110, Dmitry BaryshnikovDmitry Baryshnikov 2, & HeikkiVesanto 8. (n.d.). Network/Routing analysis with open source GIS. Retrieved from https://gis.stackexchange.com/questions/303217/network-routing-analysis-with-open-source-gis [9] Student Follow. (2014, March 12). Retrieved January 09, 2019, from https://www.slideshare.net/SwapnilRajput/network-analysis-in-gis

Geotextiles play a very important and crucial role in the civil engineering works. Geotextiles are permeable textile structures made of polymeric materials and are used mainly in civil engineering applications in conjunction with soil, rock and water, performing various functions such as separation, filtration, drainage and reinforcement. They come in three basic forms: woven, non-woven, or knitted. They are the largest group of geosynthetics in terms of volume. They are used in geotechnical engineering, heavy construction, building and roadway construction, hydrogeology and environmental engineering, etc. Geotextiles are used with foundations, soil, rock, earth or any other related material as an integral part of any project, structure, or system. The possible applications of geotextiles in civil engineering field have been successfully developed and are beneficial in terms of economics, durability and performance. They are used in earth dam, roadway, highway, rail road, and industry, stabilisation of soil of rock slopes, drainage control, tunnel construction, coastal medical treatment and foundation. This paper provides an overview of the properties, functions and uses of Geo-textiles in roadway construction.

Keywords : Geo-Textiles, Pavement, Drainage, Filtration, Reinforcement

Recent

[1] Rankilor, P. R., Membranes in Ground Engineering, John Wiley and Sons, New York, 1981 [2] Gregory, R. N., Barry, C. R., Geo-textiles in Transportation Applications, Featured Short Course, 1998. [3] Anand Krishna, Surya Nath Chaudhary: “Functions & applications of geotextiles” http://textilelearner.blogspot.com/2012/12/functions-of-geotextiles-in-civil.html [4] Sayali V Paygude, Priyanka S Dhumal, Rasulgulamdastagir Attar: “REVIEW ON GEOTEXTILES IN ROAD CONSTRUCTION”, JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN CIVIL ENGINEERING ISSN: 0975 – 6744| NOV 16 TO OCT 17 | Volume 4, Issue 2 [5] Dr. Umesh Sharma, Abhishek Kanaoungo, Ankita Khatri: “Application of Geotextiles in Pavement Drainage Systems”, International Journal of Civil Engineering Research. ISSN 2278-3652Volume 5, Number 4(2014), pp.385-390 [6] Annu, Maiyanka Verma: “Use of Geotextile Pavement in Road Construction In India”, IOSR Journal of Engineering (IOSRJEN) ISSN(e): 2250-3021, ISSN (p): 2278-8719Vol. 08, Issue 9 (September. 2018), ||V(III) || PP 01-05

Each and every city would be enhanced with word "Smart". Various problems like traffic congestion, pollution and high accident rate are mainly due to rapid urbanization and motorization which are the major issues in achieving goal to make a city "Smart". The problem can be overcome to a great extent with optimization of signal cycle length which takes pedestrian crossing time into consideration. This paper mainly focuses on pedestrian crossing which is the most dangerous component in transport field. The behaviour of pedestrian crossing under mix traffic condition at signalized intersection is needed to be considered while optimizing signal cycle and designing crosswalks. The pedestrian clearance time is affected by various pedestrian characteristics and surrounding environment under mixed traffic condition. Pedestrian behaviour need to be analysed for safe and efficient traffic management. This paper considers presents the behaviour of pedestrians at an urban signalized intersection with an objective to suggest factors to be considered for traffic safety at intersections.

Keywords : Traffic Management, Pedestrian Signal, Pedestrian Clearance Time, Intersection Characteristics, Smart City

Recent

[1] IRC 093-1985, “Guidelines on Design and Installation of Road Traffic Signals”, The Indian Roads Congress, New Delhi, India. [2] Kotkar Kishor Laxman, Rajat Rastogi, and Satish Chandra (2010). “Pedestrian Flow Characteristics in Mixed Traffic Conditions” Journal of Urban Planning and Development, 136(1), 23–33. DOI: 10.1061/(ASCE)0733-9488(2010)136:1(23) [3] Marisamynathan and Vedagiri Perumal (2014). “Study on Pedestrian Crossing Behaviour at Signalized Intersections” CICTP 2014: Safe, Smart, and Sustainable Multimodal 2641 Transportation Systems © ASCE 2014, 2641-2652. DOI: 10.1016/S2095-7564(15)30094-5 [4] Pelin Onelcin and Yalcin Alver (2017). “The crossing speed and safety margin of pedestrians at signalized intersections”, 19th EURO Working Group on Transportation Meeting, EWGT2016, 5-7 September 2016, Istanbul, Turkey; Transportation Research Procedia-22, PP 3–12. DOI: 10.1016/j.trpro.2017.03.002 [5] Varsha V. and Bindhu B. K. (2016). "Effect of Pedestrian Characteristics at Signalized Intersection”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 5, Issue 08, PP 29-33. [6] YaoWu, Jian Lu, Hong Chen and Haifei Yang (2015). “Development of an Optimization Traffic Signal Cycle Length Model for Signalized Intersections in China”, Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2015, Article ID 954295, 9 pages. DOI: http://dx.doi.org/10.1155/2015/954295

Traffic on the existing road is increase due to rapid modernization and industrialization due to extremely growth of vehicles all over the world. Some problems like congestion, delay and pollution remain a big challenge because of this. For continuous movement of vehicles through intersection, efficient traffic signal control is provided at the intersection. Volume, speed and density are the three main parameter of traffic flow. Primary objective of implementing Traffic volume study are designing, planning and improving traffic system. This paper represents the actual traffic behaviour and its characteristics. For which we carried out classified volume count study by Videography method at Majura Gate intersection, Surat, Gujarat, India. Collected data further extracted for the analysis purpose. From the analysis it is concluded that traffic flow influence by flyover-bridge and this flow contains higher percentage of motorcycle, rickshaw and car.

Keywords : Volume Count, Road Capacity, Passenger Car Unit

Recent

[1] Abrar Ul Haq Bhat and Dr. Rakesh Gupta (2018), “A Review Paper on Study of Traffic Volume and Its Safety Measures on National Highways” International Research Journal of Engineering and Technology (IRJET) Vol. 5 Issue 7, PP. 1346-1348. [2] Bhavneet Singh and Dr. Tripta Goyal (2015), “Study of Traffic Volume and Level of Service of Panjab University, Chandigarh” International Journal of Engineering Research and Applications Vol. 5 Issue 7, PP. 09-14. [3] Manjunath K. R and Lohith R M (2013). “Origin-Destination Studies -A Case Study Of Junction Improvements In Bangalore City”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 7, PP. 2253-2258 [4] Meruga Siva, Parvathi and Basavaraj Akki (2017), “Classified Traffic Volume Study at Ghatekesar Junction” International Journal of Engineering and Techniques, Vol. 3 Issue 6, PP. 420-435. [5] Neha D. Solanki, Ankita J. Patel and Vipin G. Yadav (2018), “Modernisation of Traffic Sign and Markings (India V/S Other Country) for Effective Traffic Management: State Of Art” International Research Journal of Engineering and Technology (IRJET), Vol. 5 Issue 12, PP. 441-455. [6] Pratik V. Parmar, Vaidehi J. Patel and Vipin G. Yadav (2018), “Management of Traffic at Road Intersection using Software Modelling” International Research Journal of Engineering and Technology (IRJET), Vol. 5 Issue 12, PP. 72-85. [7] Raj Pratap Singh, Himanshu Tekwani, Bhavesh Joshi, Pratheek Sudhakaran and Jitendra Singh (2018), “Study of Traffic Volume and its Safety Measurement at Dadabari Circle, Career Point University, Kota” International Journal of Current Engineering and Technology, Vol. 8 No.1, PP. 72-74. [8] Shashikant Sharma. “Classified Volume Count and Origin-Destination Survey of Bhavnagar – Vataman Road and Rajkot – Ahmedabad Road”, UNITED RESEARCH ORGANIZATION [9] Vaidehi J. Patel, Pratik P. Parmar and Vipin G. Yadav (2018), “Methods for Optimization of Signal Cycle Length” Global Research and Development Journal for Engineering (GRD), Vol. 3 Issue 12, PP. 12-16.

Rainfall is an infrequent and an important hydrological parameter on the earth. In the design of irrigation and other hydraulic structures, evaluating the magnitude of extreme rainfall for a specific probability of occurrence is of much importance. For the present study daily rainfall data from 1968-2010 for Waghodia Taluka is collected and analysed for Annual One Day Maximum Rainfall (AODMR) using various five commonly used probability distribution viz., Gumbel’s distributions, Normal distributions, Lognormal, Log Pearson type III and Generalized Extreme distribution to determine the best fit probability distribution. The expected values were compared with the observed values using goodness of fit were determined by chi square (γ2) test. The chi-square values for Normal, Log-Normal, Log- Pearson type-III, Generalized Extreme distributions and Gumbel’s distributions and were 29.98, 29.68, 48.58, 8.40 and 4.06 respectively which shows that the Gumbel’s distribution was the best fit probability distribution to forecast annual one day maximum rainfall for different return periods. Also, expected Annual One Day Maximum Rainfall using Gumbel’s distribution for return period of 2, 5, 10, 25, 50 and 100 were 122.65mm, 177.75mm, 214.24mm, 260.34mm, 294.54mm and 328.49mm respectively. The comparisons between the observed and predicted maximum value of rainfall clearly shows that the developed model can be efficiently used for the prediction of rainfall. The results of this study would be useful for agricultural scientists, decision makers, policy planners and researchers for agricultural development and constructions of small soil and water conservation structures, irrigation and drainage systems in Gujarat, India.

Keywords : AODMR, Probability Distributions, Chi-Square Test

Recent

[1] Ahmed R. (1989). Probabilistic estimates of rainfall extremes in Bangladesh during the pre-monsoon season. Indian Geographical Journal, 64, 39–53. [2] Bhakar SR, Mohammed I, Mukesh D, Neeraj C. and Bansal AK. (2008). Probability Analysis of Rainfall at Kota”, Indian J. Agric. Res., 42 (3): 201 -206. [3] Bhatt VK, Tiwari AK and Sharma AK (1996).Probability models for prediction of annual maximum daily rainfall for Datia .Indian Journal of Soil Conservation, 24(1): 25-27. [4] Immerzeel W. (2007). Historical trends and future predictions of climate variability in the Brahmaputra basin. International Journal of Climatology, 28(2), 243-254. [5] Karmakar S. and Khatun A. (1995). Variability and probabilistic estimates of rainfall extremes in Bangladesh during the southwest monsoon season. Mausam, 46(1), 47-56. [6] Kumar S. and Kumar D. (1989). Frequency of seasonal antecedent rainfall conditions. Indian Journal Soil Conservation, Vol. 17(1): 25-29. [7] May W. (2004). Simulation of the variability and extremes of daily rainfall during the Indian summer monsoon for present and future times in a global time-slice experiment. Climate Dynamics, 22(2-3), 183-204. [8] Rahman MR, Salehin, M, and Matsumoto J. (1997). Trends of monsoon rainfall pattern in Bangladesh. Bangladesh Journal of Water Resources, 14- 18, 121-138. [9] Sabarish R, Narasimhan R, Chandhru AR, Suribabu CR, Sudharsan and Nithiyanantham S., (2015). Probability analysis for consecutive-day maximum rainfall for Tiruchirapalli City (south India, Asia). Appl Water Sci (2017) 7:1033–1042 DOI 10.1007/s13201-015-0307-x. [10] Shah K and Suryanarayana TMV (2014). Characterization and Frequency Analysis of One Day Annual Maximum and Two to Seven Consecutive Days’ Maximum Rainfall of Panam Dam, Gujarat, India. International Journal of Engineering Trends and Technology (IJETT) – Volume 13 Number 2 – Jul., ISSN: 2231-5381, 76-79. [11] Shah K and Suryanarayana TMV (2014). Probability Analysis for Estimation of Annual One Day Maximum Rainfall of Devgarhbaria Station of Panam Catchment Area. International Journal of Engineering Research, issue Special 3, ISSN: 2319-6890) (online), 2347-5013(print), 11-15.

Drought assessment is very important to manage water resources in lean period. It plays vital role in managing water demands especially in agriculture sector. In the present study, monthly rainfall and potential evapotranspiration data for 102 years (i.e., 1901 to 2002) were collected and analysed for drought assessment. The dry periods were calculated using meteorological drought indices i.e. Reconnaissance Drought Index for various time scales of RDI-3, RDI-6, RDI-9 and RDI-12. The outcomes of the study shows that RDI-12 gives finest results as compared to other time scales because it considers all 12-month of hydrologic year for calculation. Amongst the period considered, the normal dry, moderate drought, severe drought and extreme drought conditions occurred for 29%, 6%, 6% and 4% respectively. Considering only the drought years i.e. (46 Years) normal dry, moderate drought, severe drought and extreme drought conditions exists for 65%, 13%, 13% and 9% respectively. It is recommended that more accurate predictions are achieved using longer duration of RDI-12 index in drought monitoring systems for water resources planning and water management in the present study.Based on the findings of this research, it is recommended that the RDI index should be used as the proper drought index in drought monitoring systems for water resources planning and management in Banaskantha.

Keywords : Drought Assessment, Reconnaissance Drought Index

Recent

[1] Lunagaria N and Suryanarayana TMV (2017). Assessment of Drought in Sabarkantha District Using Reconnaissance Drought Index (RDI). International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064, Volume 6 Issue 1,970-974. [2] Malakiya, A.D. and Suryanarayana, T.M.V.: Assessment of Drought Using Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI): A Case Study of Amreli District. International Journal of Science and Research (IJSR), 5(8) (2016) 1995-2002. [3] Mistry PB, Suryanarayana TMV (2019) Categorization of Drought During Twentieth Century Using Precipitation in Banaskantha District, Gujarat, India. Innovations in Infrastructure. Advances in Intelligent Systems and Computing, vol. 757. PP 267-274. [4] Patel NR. Suryanarayana, TMV and Shete DT. Analyzing Extreme Events Using Standardized Precipitation Index during the 20th Century for Surat District, India. Application of Geographical Information Systems and Soft Computation Techniques in Water and Water Based Renewable Energy Problems. Eds. M. Majumder, Springer Verlag, Singapore, (2017) 41-50. [5] Thomas T, Jaiswal RK, Galkate RV and Nayak TR (2015). Reconnaissance drought index based evaluation of meteorological drought characteristics in Bundelkhand, International Conference on Emerging Trends in Engineering, Science and Technology, Procedia Technology 24, 23 – 30. [6] Tigkas D, Vangelis H and Tsakiris G (2013). The RDI as a composite climatic index”, European Water 41: 17-22. [7] Wilhite DA and Glantz MH (1985). Understanding the drought phenomenon: The role of definitions. Water International, 10, 111–120.

The current state of globalization had increased the demand for ground water due to the growth in the population. In order to be more responsible in protecting the ground water resources various artificial methods had been adopted. The areas located around the Vadamaradchy lagoon in Jaffna peninsula of northern part of Sri Lanka are highly vulnerable to water scarcity and saline ground water issues due to the lack in the proper utilization of available sources. This exploration is concerned on quantitative estimation of storage capacity of Vadamaradchy lagoon and to find the most efficient hydraulic structure which can be easily accessible to solve the salinity of groundwater and to increase the water level in a productive manner of these prone areas. The study area is characterised by an average rainfall of 1298±683 mm. The catchment runoff from average rainfall (1298 mm) is 78.72 MCM and from 65% dependability rainfall (1128 mm) is 61.08 MCM. The capacity of ponds and waterways in the catchment area of Vadamaradchy lagoon is 13.66 MCM. Vadamaradchy lagoon is directly receiving 101.25 MCM from average rainfall and 87.99 MCM during 65% dependability rainfall. The lagoon is incapable to store 166.31 MCM and 135.40 MCM of water during average rainfall and 65% dependability rainfall respectively. So, in order to retain the surcharge water, it is necessary to construct 3 m height embankment around lagoon boundary. Annually pond can hold maximum 140.4 MCM of water which can easily satisfy the water requirement (30.66 MCM) of total population.

Keywords : Vadamaradchy Lagoon, Ground Water, Fresh Water Cultivation, Jaffna

Recent

[1] Department of Agrarian Development- Jaffna. [2] District secretariat, Jaffna. http://www.jaffna.dist.gov.lk/index.php/en/ [3] Janen, S. S., & Sivakumar, S. S. (2014). Ground Water Quality Improvement of Jaffna Peninsula of Sri Lanka by Regulating Water flow in the lagoon Mouths. International Journal of Scientific & Engineering Research, Vol. 5, 973-978. [4] Lacey, G., (1930). Stable channels in alluvium. Minutes Proc., Inst. Civ. Engrs, Lond. 229, 259–292. [5] Metrological Department of Sri Lanka. (2012). Climate in Sri Lanka. Retrieved from Metrological Department: www.meteo.gov.lk [6] Navaratnarajah, V. (1994). Water Problems in the Jaffna Peninsula. Affordable Water Supply and Sanitation, Proceedings of the 20th WEDC Conference. Colombo, Sri Lanka: WEDC Loughborough, UK. [7] Sivakumar, S. S. (2013). Reclamation of Land and Improve Water Productivity of Jaffna Peninsula of Northern Sri Lanka by improving the water quality of the lagoons. Irrigation Department. [8] Spencer E (1967) A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 15:11– 26. [9] Strang G, Fix J (1973) An analysis of the finite element method. Prentice-Hall, Englewood Cliffs.

A study was carried out to determine the irrigation water requirement of tomato in Aat Distributary Navsari of Navsari branch canal KLBMC, by using cropwat 9.0, water balance method and water balance method using root zone depth by cropwat 9.0. In this study twelve years of meteorological data from 2005 to 2017 were used. The effective rainfall obtained by cropwat 9.0, water balance method and water balance method using root zone depth by cropwat 9.0 is 1093.8 mm, 301.370 mm and 314.310 mm respectively. Actual seasonal evapotranspiration rate obtained by cropwat 9.0, water balance method and water balance method using root zone depth by cropwat 9.0 is 634.8 mm, 581.250 mm and 581.250 mm respectively. The net irrigation requirement is 456.4 mm using cropwat 9.0, 257.895 mm using water balance method and 266.035 mm using water balance method using root zone depth by cropwat 9.0. Irrigation scheduling by using water balance method can save water use up to 198.505 mm and 8.14 mm over cropwat 9.0 and water balance method using root zone depth by cropwat 9.0 respectively. The water balance method is more effective and efficient than the cropwat 9.0 and water balance method using root zone depth by cropwat 9.0, only because whereas the earlier method used available soil water at time t over the effective root zone depth and remaining available soil water for irrigation scheduling and cropwat 9.0 used daily soil water available and readily available water for the same purpose.

Keywords : CROPWAT 9.0, Water Balance Method, Irrigation Scheduling, Net Irrigation Requirement, Tomato, AAT Distributary

Recent

[1] Dawod Rasooli Kia (2013) Water requirements for major crops in different agro- climatic zones of Iraqi kurdistan using by cropwat 9.0. Journal of Agriculture and Veterinary Science 6:30-36. [2] Ephraim Sekyi-Annan, Bernhard Tischbein, Bernd Diekkrüger ID et al. (2018) Year-round irrigation schedule for a tomato–maize rotation system in reservoir-based irrigation schemes in ghana. Water 10:2-25. doi: 10.3390/w10050624 [3] Falguni Parekh1, Kevin Pramodchandra Prajapati (2013) Climate change impacts on crop water requirement for sukhi reservoir project. International Journal of Innovative Research in Science, Engineering and Technology 2:4685-489. [4] Fitsume Yemenu Desta, Kidist Abera, Michael Eshetu, et al.(2017) Irrigation water planning for crops in the central highlands of Ethiopia, aided By Fao Crop Wat Model. African Journal of Agricultural Research 12:2330-2335. doi: 10.5897/AJAR2016.11659 [5] Gamal Abdel Rahman, Talaat AM and Zawe .C (2016) Water requirements for main crops grown under three different agro ecological zones, zimbabwe. Middle East Journal of Agriculture Research 5:14-28 [6] Ibraheem Alhassan, Abubakar Ibrahim, Musa Mohammed Maunde et al. (2015) Water requirement and irrigation schedule for tomato in northern guinea savanna zone, nigeria. 2:65-70. [7] Memon A.V, Jamsa S (2018) Crop water requirement and irrigation scheduling of soybean and tomato crop using cropwat 9.0. International Research Journal of Engineering and Technology 5: 669-671. [8] Ratna Raju C., Yella Reddy K., Satyanarayana T.V et al. (2016) estimation of crop water requirement using cropwat software in appapuram channel command under krishna western delta. International Journal of Agriculture Sciences 8: 1644-1649. [9] Robiul Islam, Mahmudul Hasan Mizan, Mafruha Akter et al. (2017) Assesment of crop and irrigation water requirements for some selected crops in northwestern bangladesh. Global Journal of Science Frontier Research: D Agriculture and Veterinary 17:15-22. [10] U. Surendran, C. M. Sushanth, George Mammen et al. (2017)FAO-CROPWAT model-based estimation of crop water need and appraisal of water resources for sustainable water resource management: Pilot study for Kollam district – humid tropical region of Kerala, India. current science 112:76-86. doi: 10.18520/cs/v112/i01/76-86

Missing Rainfall data may vary in length from one or two days to several years. Especially in data-sparse areas, estimation of the missing data is necessary in order to utilize partial records. For filling missing rainfall data, various methods are used. To generate one output, some methods need only one input variable like Closest Station Method (CSM) & Artificial Neural Network Method (ANN) and some methods must need more than one input variables like Arithmetic Average Method (AAM), Inverse Distance Method (IDM) & Normal Ratio Method (NRM). Gujarat is divided into eight agroclimatic zones. South Gujarat Agroclimatic zone partly consisting of Bharuch, Navsari and Surat districts is selected for the present study. There are 22 talukas under the study area and 75 rain gauge stations cover selected 3 districts. Daily rainfall data from 1981 to 2015 of respective rain gauge stations are collected from State Water Data Center, Gandhinagar. In order to compute the missing daily rainfall data, the latitudes and longitudes of the different rain gauge stations are converted to x and y co–ordinates using the Franson Coord Trans V 2.3. Cluster analysis is used to group the rain gauge stations into clusters for filling in missing rainfall data. The paper discusses determining missing rainfall data of rain gauge stations of Navsari district by Closest Station Method.

Keywords : Rain Gauge Stations, Rainfall Data, Missing Data, Cluster Analysis, Closest Station Method

Recent

Gift D, Jeffrey PW, Li C (2014) Assessing artificial neural networks and statistical methods for infilling missing soil moisture records. Journal of Hydrology. S0022-1694(14)00353-9 [2] Harshannand KG, Regulwar DG (2013) Artificial Neural Network Method for Estimation of Missing Data. International Journal of Advanced Technology in Civil Engineering. ISSN: 2231 –5721(13), Volume-2, Issue-1 [3] Haydar D, Zoe R (2018) Missing value imputation for short to mid-term horizontal solar irradiance data. Applied Energy. 225 (18) 998–1012 [4] Khalifeloo MH, Munira M, Mohammad H (2015) Multiple Imputation for Hydrological Missing Data by using a Regression Method (Klang River Basin). IJRET: International Journal of Research in Engineering and Technology. [5] Kim JW, Yakov AP (2010) Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology. 394 (10) 305–314 [6] Miró JJ, Vicente C, María JE (2017) Multiple Imputation of Rainfall Missing Data in the Iberian Mediterranean Context. Atmospheric Research. S0169-8095(17), 30125-4 [7] Nkuna TR, Odiyo JO (2011) Filling of missing rainfall data in Luvuvhu River Catchment using artificial neural networks. Physics and Chemistry of the Earth. 36(11), 830-835 [8] Patel NR, Suryanarayana TMV, Shete DT (2008) Comparison of ANN and conventional methods for predicting missing climate data. Proc. of International conference on “Operations Research for a Growing Nation in conjunction with 41st Annual convention of Operation Research Society of India, Tirupati. [9] Sattari MT, Ali RJ, Andrew K (2016) Assessment of different methods for estimation of missing data in precipitation studies. Hydrology Research 1-13 [10] Shete DT, Patel NR (2012) Missing Climate Data. Journal of Applied Hydrology, Aandhra University 1-22

This paper presents the experimental investigation regarding longitudinal velocity distribution in straight and curved reaches of an open channel. Extensive data has been collected in the laboratory flume with straight and sinusoidal path. Velocity data has been collected by Programmable Electro-Magnetic shunt meter at predetermined nodal points in both straight and curved reaches. The data has been collected for only one discharge Q as 20 l/s and single R/W value as 3.12. All the data has been carefully analysed and longitudinal velocities have been plotted. From these plots the actual variation of longitudinal velocities has been studied, in both straight and curved portions of the channel. It is found that there is significant effect of curvature on the velocity distributions.

Keywords : Curved Open Channel, Longitudinal Velocities, Curvature Ratio

Recent

[1] Bonakdari, H. et al. (2014) Numerical analysis and prediction of the velocity field in curved open channel using artificial neural network and genetic algorithm. Eng. Applications of Computational Fluid Mechanics, Vol. 5, No. 3, pp. 384–396. [2] Chow, V. T. (2009) Open Channel Hydraulics. McGraw-Hill Book Co., Inc., New York, N.Y. [3] Faruque, M. et al. (2014) Open channel flow velocity profiles for different Reynolds numbers and roughness conditions. Intl J of Res in Eng and Tech. Vol. 03, Issue: 01, pp. 400-405. [4] Gholami A. et al. (2014) Experimental and numerical study on velocity fields and water surface profile in a strongly-curved 90º open channel bend. Eng. Applications of Computational Fluid Mechanics, Vol. 8, No. 3, pp. 447–461. [5] Gonzalez, J. A. et al. (1996) Analysis of open-channel velocity measurements collected with an acoustic doppler current profiler. Intl Water Resources Association. pp. 1-8. [6] Henderson, F. M. (1966) Open Channel Flow. The MacMillan Co., New York, N.Y. [7] H. Cardoso et al. (1989) Uniform flow in smooth open channel. J. Hydraul. Res. Vol. 27, No. 05, pp. 603-616. [8] Kirkgoz M. S. (1989) Turbulent velocity profiles for smooth and rough open channel flow. J. Hydraul. Engg. Vol. 115, No. 11, pp. 1543-1561. [9] Kirkgoz M. S. et al. (1997) Velocity profiles of developing and developed open channel flow. J. Hydraul. Eng. Vol. 123, No. 12, pp. 1099-1105. [10] Nezu I. et al. (1986) Open-channel flow measurements with a laser doppler anemometer. J. Hydraul. Eng. Vol. 112, No. 05, pp. 335-355. [11] Sahu, M et al. (2011) Point form velocity prediction in meandering open channel using artificial neural network. 2nd Intl Conference on Environmental Science Tech. IPCBEE, Vol. 6, pp. 209-212. [12] Sarma K. V. N. et al. (1983) Velocity distribution in smooth rectangular open channels. J. Hydraul. Eng. Vol. 109, No. 02, pp. 270-289. [13] Yang, K. et al. (2013) Modelling depth-averaged velocity and boundary shear stress in rectangular compound channels with secondary flows. J. Hydraul. Eng., 2013, 139(1), pp. 76-83.

This paper presents the experimental investigations of effect of bend on scour and deposition patterns around causeways and bed bars. Data have been collected in a re-circulatory open channel flow system on three causeway model slabs for many discharge values ranging from 1.0 l/s and 8.0 l/s. The causeway model slabs were made of cement concrete with 0.75 m length, 0.20 m width and 0.20 m in overall depth. To critically observe the effects of presence of bend on scour and deposition, three locations of causeway slabs and one location of bed bar were used. The first one is provided in a straight reach in the test channel, second one just after the first bend normal to flow and third one was an oblique slab provided between the inner and our curvature of the second bend downstream of the first bend. Data of scour and deposition around causeways and bed bar were recorded after the end of the run. Also photographs were taken. It is concluded that for each discharge, scour and deposition occur in all cases, but at low flow the scour in straight reach is less pronounced. However, scour occur in second and third causeways due to the presence of bends. At still high values of discharges, scour and deposition both are significant in all the causeways. Since first causeway is straight and normal to flow, there is uniform scour and deposition along the edges of the causeways. The scour and successive deposition occur in other two causeways with high magnitudes but the location of maximum scour depth shifted. Also scour holes were observed around the bed bars. Photographs also support the findings in this investigation.

Keywords : Causeway, Channel Bend, Bed Bar, Scour and Deposition

Recent

[1] Athar, M., Adil, M. & Athar H. (2017). “Study of Submersible Hydraulic Structures, A critical Review” U.G.C. Approved Intl J of Engg Tech Science and Research (IJETSR), ISSN 2394 2386, Vol. 4, Issue 11. [2] M. Athar & Saluja, I.S. (2017) “Scour due to Rock sills in Curved Horizontal Channels”, Intl Conf on Hydraul, Water Resources and Coastal Engg (XXII-Hydro-2017), L. D. College of Engg under the aegis of the Indian Society of Hydraulics at L. D. College of Engg, Ahmadabad, Gujarat, India. [3] Saluja, I.S., Athar, M. & Ansari, S. A. (2017) “Flow characteristics in Curved Channels, A review Paper.”, U.G.C. Approved International Journal of Computer and Mathematical Science (IJCMS), ISSN-2347 8527, Vol. 6. [4] Saluja, I.S. & Athar, M. (2017) “Computation of Scour due to Rock Sills in Alluvial Channels”. U.G.C. Approved International Journal of Engineering Technology Science and Research (IJETSR), ISSN 2394 3386, Vol. 4, Issue 12. [5] Saluja, I.S. & Athar, M. (2017) “Scour in Curved Open Channels-A Review”, U.G.C. Approved Intl J of Engg Tech Sci and Res (IJETSR), ISSN 2394 3386, Vol. 4, Issue 12. [6] IRC: SP: 82-2008, Guidelines for design of causeways and submersible bridges. Indian Road Congress. [7] IRC: 2010, Standard specification and code of practice for road bridges. Section-II, Load and stresses, Indian Road Congress. [8] IS: 456-2000 Code of Practice for Design of Reinforced Concrete Structures. Bureau of Indian Standards.

This research work indicates the advancement of flood modeling using Arc GIS and HEC GeoRAS software in the field of water resources engineering and flood management. The methodology is applied to Purna River, Navsari district, Gujarat, India. In this research, we have used satellite, topographic, contour map, hydraulic and hydrologic data for flood modeling. In this paper first part of the methodology followed by preprocessing data involved to develop DEM (Digital Elevation Model) using contour data, Georeferencing, and Shapefile in Arc GIS software. After that, these all geometry data are imported into HEC RAS through HEC GeoRAS interface in ArcGIS environment. This research demonstrates the flood mapping approach for disaster authorities by modeling using past data required in HEC-RAS. This methodology is also used to develop flood risk map in Arc GIS environment for the study area. From this research, we can conclude that with GIS technology combine with the computed model the flood mitigation is very beneficial for disaster management after mapping the extent of the flood.

Keywords : Arc GIS, HEC-RAS, HEC GeoRAS, Flood, D. E. M, G.I.S, R. S

Recent

[1] Brivio PA, Colombo R, Maggi M, et al (2002) Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23 (3), 429-441. [2] Daxikar A, Hillier T, Makarem F, et al (2008) Application of GIS technology in coastal flood hazard mitigation. Solutions to Coastal Disasters Congress 2008. April 13-16, 2008, Turtle Bay, Oahu, Hawaii, United States (pp. 396-409).https://doi.org/10.1061/40968(312) 36. [3] Debasis D, Bipul T (2012) Hydro-Geomorphological Analysis of a Water Logged Area of South Tripura District Using RS and GIS Technology, World Environmental and Water Resources Congress 2012, May 20-24, 2012, Albuquerque, New Mexico, United States, https://ascelibrary.org/doi/10.1061/9780784412312.388 [4] Hazarikad MK, Samarakoond L (2010) Application of remote sensing and GIS for flood risk analysis: A case study at Kalu-Ganga River, Sri Lanka. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 38(Pt 8), 110-115. [5] Islam MM, Sado, K (2000) Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. Hydrological Sciences Journal, 45(3), 337-355. [6] Jha AK, Bloch, R, Lamond, J et al (2012) Cities and flooding: a guide to integrated urban flood risk management for the 21st century. The World Bank. [7] Lee S, and Choi U (2003) Development of GIS-based geological hazard information system and its application for landslide analysis in Korea. Geosciences Journal, 7(3), 243-252. [8] Merwade V, Cook A, Coonrod J (2008). GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping. Environmental Modelling & Software, 23(10-11), 1300-1311. [9] Patel DP, Dholakia MB (2010) Feasible structural and non-structural measures to minimize the effect of flood in lower Tapi basin. WSEAS Trans Fluid Mech, 3, 104-121. [10] Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2). [11] Roy SK, Sarker SC (2016) Integration of Remote Sensing Data and GIS Tools for Accurate Mapping of Flooded Area of Kurigram, Bangladesh. Journal of Geographic Information System, 8(02), 184. [12] Sahoo SN, Sreeja P (2015) Development of Flood Inundation Maps and quantification of flood risk in an Urban catchment of Brahmaputra River. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(1), A4015001. [13] Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological processes, 11(10), 1427-1439. [14] Zerger A, Wealands S (2004) Beyond modelling: linking models with GIS for flood risk management. Natural Hazards, 33(2), 191-208.

India is an agricultural dominated country. Agriculture is a backbone of India. Nearly 65% of population is dependent on this sector. Hence study of crops and cropping pattern play a vital role to increase the crop production. In this study an attempt will be made to map the different cropping pattern followed in Olpad taluka of Surat, Gujarat. A multidate Landsat satellite data from USGS will be used to generate cropping pattern and study of crop rotation during Kharif and Rabi will be carried out. To accomplish this Arc GIS software of (ESRI) will be used. This map is validated by taking the ground truth data using GPS receiver in the study area to improve the accuracy of the mapping. Various vegetation indices will be calculated using GIS and RS to access the efficiency of cropping system.

Keywords : Cropping Pattern, GIS, Remote Sensing, Vegetation Indices

Recent

[1] Amit Kumar, Hardeep Singh Sheoran, Manoj Yadav and R. S. Hooda(2015), Geospatial Approach for Block Wise Area Estimation of Kharif Season Crops in Fatehabad District, Haryana (India), International Journal of Science, Engineering and Technology Research, Vol.4 [2] A.R. HUETE (1988) A Soil-Adjusted Vegetation Index (SAVI) remote sensing of environment 25:295-309 [3] Bannari, A., Morin, D., Bonn, F. and Huete, A. R.(1995) 'A review of vegetation indices', Remote Sensing Reviews, 13: 1, 95-120 [4] M. P. Sharma, Manoj Yadav, R. Prawasi, Pavan Kumar and R. S. Hooda (2011), cropping system analysis using remote sensing and gis: a block level study of kurukshetra district, ARPN Journal of Agricultural and Biological Science, Vol. 6 [5] M.P.Sharma,Kirti Yadav,Kamalpuneet Kaur ,Ravindra Prawasi, Ajeet Singh(2014) Geospatial Approach for Cropping System Analysis A Case Study of Bhiwani District, Haryana., International Journal of Science, Engineering and Technology Research, Vol. 3 [6] Panigrahy, K. R. Manjunath & S. S. Ray (2005) Deriving cropping system performance indices using remote sensing data and GIS, International Journal of Remote Sensing, 26:12, 2595-2606 [7] Ramya , Mr.K.Srinivasan (2018), A cropping system analysis for Palacode taluk using remote sensing and GIS, International conference on advancements in engineering, technology and sciences. [8] Satyawan, Manoj Yadav, R S Hooda (2012), Cropping System Analysis Using Geospatial Approach: A Case Study of Sirsa District in Haryana, India., International Journal of Science and Research vol.3 [9] S.S. RAY (2005) use of gis and remote sensing for crop diversification - a case study for punjab state, Journal of the Indian Society of Remote Sensing, Vol. 33 [10] S.S. Ray, Anil Sood, Sushma Panigrahy (2005), Derivation of indices using remote sensing data to evaluate cropping systems, Journal of the Indian Society of Remote Sensing, Vol. 33

Evaporation is the most influencing parameter of the hydrologic cycle and it is a key component playing an important role for development and the management of various water resource projects in arid/semi-arid climatic regions. Fuzzy logic is being used widely as the decision-making tool. In various past studies, Fuzzy Logic has been used to predict the values of various hydrological parameters with much less error. Present study intended to determine the application of Fuzzy Logic to predict the monthly evaporation. A Fuzzy model was developed from the observed data of average monthly maximum temperature, wind speed, relative humidity, and water temperature from Jan-2017 to Jan-2018. The predicted values of evaporation, by Fuzzy model, are compared with the actual field observations to evaluate its performance.

Keywords : Fuzzy, Fuzzy Model, Evaporation, Arid Climate

Recent

[1] Goyal Manish Kumar, Birendra Bharti, Quilty John, Adamowski Jan, Pandey Ashish (2014) Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Elsevier, Expert Systems with Applications 41 (2014) 5267–5276, http://dx.doi.org/10.1016/j.eswa.2014.02.047 [2] Keskin Erol M, Özlem Terzi and Dilek Taylan, (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey, Taylor and Francis, Hydrological Sciences Journal, 49:6, -1010, DOI: 10.1623/hysj.49.6.1001.55718 [3] Kulkarni, A. D. and Anaokar, G. S. (2016) Prediction of Evaporation Loss in Reservoir with Fuzzy Logic Approach, European Journal of Advances in Engineering and Technology, 2016, 3(12):39-42, ISSN: 2394 - 658X [4] Patel Jayantilal N and Balve Pranita N. (2016) Evapotranspiration Estimation with Fuzzy Logic, International Journal of Advances in Mechanical and Civil EngineeringVolume-3, Issue-4, Aug.-2016, ISSN: 2394-2827. [5] Moghaddamnia A, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han, (2008) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques, Elsevier, Advances in Water Resources 32 (2009)88–97, doi: 10.1016/j.advwatres.2008.10.005 Book [6] S. Vedula and P.P. Mujumdar (2007) Water Resource System Modelling techniques and analysis

In the present study ‘Teaching Learning Based Optimization’ (TLBO) optimization method has been applied to the water resources engineering problem. TLBO is a population-based natural-inspired evolutionary algorithm comparatively simple, easy and robust. TLBO algorithm is capable of providing a global solution. Four water resources problem such as optimizing crop water demand, maximization of benefits, minimization of reservoir capacity and minimization of reservoir capacity with evaporation losses solved using TLBO technique. The results were compared with linear programming & dynamic programming solutions. TLBO algorithm has proven to be providing the global and better results. The results obtained from TLBO were better in reservoir capacity problem with evaporation losses. The results were satisfactory for optimizing crop water demand, maximization of benefits and minimization of reservoir capacity. The TLBO technique provides a satisfactory solution as other popular optimization techniques.

Keywords : TLBO, LINGO Software, Soft Computing, Linear Programming, Dynamic Programming

Recent

[1] Afshar MH (2012) Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms. J Hydro-Environment Res 6:75–87. doi: 10.1016/j.jher.2011.04.003 [2] Ashofteh P-S, Haddad OB, Loáiciga HA (2015) Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming. J Water Resour Plan Manag 141:04015030. doi: 10.1061/(ASCE)WR.1943-5452.0000540 [3] Bai T, Kan Y, Chang J, et al (2017) Fusing feasible search space into PSO for multi-objective cascade reservoir optimization. Appl Soft Comput 51:328–340. doi: 10.1016/j.asoc.2016.12.005 [4] By E, Thirumalaiah K, Deo MC (2000) Hydrological Forecasting Using Neural Networks. J Hydrol Eng 5:180–189. doi: 10.1061/(ASCE)1084-0699(2000)5:2(180) [5] Chang LC, Chang FJ, Wang KW, Dai SY (2010) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390:66–74. doi: 10.1016/j.jhydrol.2010.06.031 [6] Chau KW (2006) Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J Hydrol 329:363–367. doi: 10.1016/j.jhydrol.2006.02.025 [7] Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2012) Real-Time Operation of Reservoir System by Genetic Programming. Water Resour Manag 26:4091–4103. doi: 10.1007/s11269-012-0132-z [8] Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016) Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production. J Irrig Drain Eng 142:04016041. doi: 10.1061/(ASCE)IR.1943-4774.0001064 [9] H. Kashani M, Ghorbani MA, Dinpashoh Y, Shahmorad S (2016) Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran. J Hydrol 540:340–354. doi: 10.1016/j.jhydrol.2016.06.028 [10] Kumar V, Yadav SM (2018) Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm. Water Resour Manag 32:4375–4391. doi: 10.1007/s11269-018-2067-5 [11] Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ Model Softw 15:101–124. doi: 10.1016/S1364-8152(99)00007-9 [12] Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models. J Hydrol Eng 14:647–652. doi: 10.1061/(ASCE)HE.1943-5584.0000040 [13] Nagesh Kumar D, Janga Reddy M (2007) Multipurpose Reservoir Operation Using Particle Swarm Optimization. J Water Resour Plan Manag 133:192–201. doi: 10.1061/(ASCE)0733-9496(2007)133:3(192) [14] Nourani V (2017) An Emotional ANN (EANN) approach to modeling rainfall-runoff process. J Hydrol 544:267–277. doi: 10.1016/j.jhydrol.2016.11.033 [15] Ostadrahimi L, Mariño MA, Afshar A (2012) Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach. Water Resour Manag 26:407–427. doi: 10.1007/s11269-011-9924-9 [16] Pramanik N, Panda RK (2009) Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrol Sci J 54:247–260. doi: 10.1623/hysj.54.2.247 [17] Rodríguez-Vázquez K, Arganis-Juárez ML, Cruickshank-Villanueva C, Domínguez-Mora R (2012) Rainfall–runoff modelling using genetic programming. J Hydroinformatics 14:108. doi: 10.2166/hydro.2011.105 [18] Russell SO, Campbell PF (1996) Reservoir Operating Rules with Fuzzy Programming. J Water Resour Plan Manag 122:165–170. doi: 10.1061/(ASCE)0733-9496(1996)122:3(165) [19] SaberChenari K, Abghari H, Tabari H (2016) Application of PSO algorithm in short-term optimization of reservoir operation. Environ Monit Assess 188:667. doi: 10.1007/s10661-016-5689-1 [20] Sahay RR, Srivastava A (2014) Predicting Monsoon Floods in Rivers Embedding Wavelet Transform, Genetic Algorithm and Neural Network. Water Resour Manag 28:301–317. doi: 10.1007/s11269-013-0446-5 [21] Smith J, Eli RN (1995) Neural-Network Models of Rainfall-Runoff Process. J Water Resour Plan Manag 121:499–508. doi: 10.1061/(ASCE)0733-9496(1995)121:6(499) [22] Taghi Sattari M, Pal M, Apaydin H, Ozturk F (2013) M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resour 40:233–242. doi: 10.1134/S0097807813030123 [23] Talei A, Chua LHC, Quek C (2010) A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling. Expert Syst Appl 37:7456–7468. doi: 10.1016/j.eswa.2010.04.015 [24] Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25:1670–1676. doi: 10.1016/j.engappai.2012.02.009 [25] Thirumalaiah K, Deo MC (1998) River Stage Forecasting Using Artificial Neural Networks. J Hydrol Eng 3:26–32 [26] Venkata Rao R (2016) Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett 5:1–30. doi: 10.5267/j.dsl.2015.9.003 [27] Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression. J Hydrol 358:96–111. doi: 10.1016/j.jhydrol.2008.05.028 [28] Wu S-J, Lien H-C, Chang C-H (2012) Calibration of a conceptual rainfall–runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters. J Hydroinformatics 14:497. doi: 10.2166/hydro.2011.010 [29] Yazdani MR, Zolfaghari AA (2017) Monthly River Forecasting Using Instance-Based Learning Methods and Climatic Parameters. J Hydrol Eng 22:04017002. doi: 10.1061/(ASCE)HE.1943-5584.0001490 [30] Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J. Hydrol. 328:704–716

Extraction of watershed parameters using Remote Sensing and Geographical Information System (GIS) and use of mathematical models is the current trend for hydrologic evaluation of watersheds. The Soil and Water Assessment Tool (SWAT) having an interface with QGIS is adopted in the present study for the determination of various outputs, viz. Surface runoff, Base flow, Percolation, Potential evapotranspiration and Sediment load in Vishwamitri river watershed in Vadodara, Gujarat, India. Various data types required as input for the study comprises of Digital Elevation Models (DEM), Landuse/Landcover maps, Soil maps and Climate data. The Stream considered lies between 22° 13' to 22° 21' of north latitude and 73° 12' to 73° 13' of east longitude. Providing require inputs, SWAT model was simulated for 2003 to 2014. Analysis reveals that maximum rainfall was obtained in 2005 and minimum rainfall is obtained in 2009. In 2005, it is found out that the Precipitation is maximum, and the corresponding surface runoff in sub-basins 3 and 5 are having highest values of 767.04mm and 747.87mm respectively. Similarly, baseflow in sub-basin 3 & 5 are having lowest values of 298.20mm and 316.40mm respectively. The percolation in sub-basin 3 & 5 are also having the lowest values of 363.10mm and 381.70mm respectively. The Surface runoff, Base flow and Percolation were found out to be around 48%, 20% and 32% of the precipitation occurred in 2005, while amongst the average precipitation of the total period considered, the Surface runoff, Base flow and percolation are found out to be around 38%, 18% and 24% respectively.

Keywords : QGIS, QSWAT, Surface Runoff, Base Flow, Percolation, PET, Sediment Load

Recent

[1] Franczyk, Jon J.,(2008). The Effects of Climate Change and Urbanization on the Runoff of the Rock Creek Basin. Dissertations and Theses. Paper 2237. [2] Jacobs, J.H. and R. Shrinivasan,(2005). Application of SWAT in developing countries using readily available data. Proceeding, 3rd International SWAT conference, Zurich. [3] Panhalkar S.,(2014). Hydrological modeling using SWAT model and geoinformatic techniques. National Authority for Remote Sensing and Space Sciences. The Egyptian Journal of Remote Sensing and Space Sciences. 17, 197–207. [4] Shivhare, V., Goel, M., Singh, C.(2014). Simulation of surface runoff for upper tapi subcatchment area (burhanpur watershed) using SWAT. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-8, 2014 ISPRS Technical Commission VIII Symposium, 09 – 12, Hyderabad, India. [5] Verma, A., Jha, M.(2015). Evaluation of a GIS-Based Watershed Model for Streamflow and Sediment-Yield Simulation in the Upper Baitarani River Basin of Eastern India. Journal of Hydrologic Engineering. Volume 20, SPECIAL ISSUE: Soil Erosion and Sediment Yield Modeling. [6] Subramanya k. “Engineering Hydrology” fourth edition, the McGraw-HiLL companies. [7] SWAT-X. 2015. Arc View SWAT, version 2015. From Soil and Water USDA Soil conservation service, 1972. “Hydrology, In National Engineering Handbook”, U.S. Department of Agriculture-soil Conservation Service: Washinton, DC. [8] User manual of SWAT

Perennial, historical and current define information about the land use and land cover changes of the Earth’s surface is thoroughly essential for any kind of sustainable development program because in which LULC serves as one of the major input criteria. LULC dynamics are analysed by using Remote Sensing and Geographic Information Systems (GIS). This study is an attempt to evaluate the changes in land use/land cover in Surat city situated in the state of Gujarat at western part of India over a 10 year period. The study made use of LandSat imageries of 2006 and 2016 taken from USGS earth explorer. Maximum Likelihood classification method is used to classify the images. Five LULC categories like water, vegetation, built up area and bare land were identified and mapped. The results shows that land cover changes occurred in water, vegetation, built up area and bare land were 3.12%, 15.5%, 12.50%, 24.95% respectively.

Keywords : Land use Land Cover, LandSat Image, Remote Sensing, GIS, Surat city

Recent

[1] K M Kafi, H Z M Shafri, A B M Shariff (2014), "An analysis of LULC change detection using remotely sensed data; A Case study of Bauchi City" International Remote Sensing & GIS Conference and Exhibition, Volume 20 (1-9). [2] Selcuk Reis (2008), "Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey " Sensors, Volume 8 (6188-6202). [3] O.R. Abd El-Kawy, J.K. Rod , H.A. Ismail , A.S. Suliman (2010), "Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data" Applied Geography (Elsevier), Volume 31 (483-494). [4] P. S. Roy, Arijit Roy (2010)," Land use and land cover change in India: A remote sensing & GIS prespective" Journal of the Indian Institute of Science, Volume 90 (489-502). [5] P. B. Tamsekar, Dr. S. B. Thorat (2017), "Land Use and Land Cover (LULC) Identification and Analysis Using Satellite Imagery", IJSRSET, Volume 3. [6] Priyank p. Patel, bhasker v. Bhatt (2016), "Land use change detection in Surat using geospatial techniques", IJARESM. [7] Piyush Yadav, shailesh deshpande (2016), "Spatio-temporal assessment of urban growth impact in Pune city using remotely sensed data ". [8] Registrar General & Census Commissioner. 2011. Population Enumeration Data (Final Population). Census India 2011. [Online] Ministry of Home Affairs, Government of India, 2011. http://censusindia.gov.in/. [9] Surat Municipal Corporation https://www.suratmunicipal.gov.in/TheCity/AboutCity

Land Use and Land Cover (LU-LC) are vital information required for various planning activities. Infrastructural planning, water resources management, agriculture planning, etc. are important applications of LU-LC information. In today’s fast changing face of earth surface due to natural and man-made activities, keeping the LU-LC information updated manually on real time basis is a herculean task. Remote sensing and GIS is very useful technique for acquisition of information about an object or phenomenon without making physical contact with the object. The integration of remote sensing with reliable information is most dependable solution in the current scenario. In this study an attempt is made to map NDVI (Normalized Difference Vegetation Index) and LU-LC for Surat, Gujarat. Satellite imagery of 2nd February 2018 by LANDSAT 8 is used with Arc GIS software of (ESRI) for the purpose of present study.

Keywords : NDVI, LULC, Remote Sensing, GIS, LANDSAT

Recent

[1] Beck, P.S.A., C. Atzberger, K.A. Hogda, B. Johansen and A.K. Skidmore, (2006) "Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI" Remote Sensing of Environment 100 (321 – 334) [2] Deng, X., J. Huang, S. Rozelle, and E. Uchida. (2008). "Growth, population and industrialization, and urban land expansion of China." Journal of Urban Economics vol. 63 Pages 96-11 [3] Erencin, Z. (2000). "C-Factor Mapping Using Remote Sensing and GIS. A case Study of Lom Sak/Lom Kao, Thailand." International Institute for Aerospace Survey and Earth Sciences [4] Gitelson, A. A. (2004). “Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation”. Journal of plant physiology. [5] Huete, C. Justice and W.V. Leeuwen, (1999) "MODIS vegetation index (MOD13): algorithm theoretical basis document". Journal of Geoscience and Environment Protection, Vol.4 No.6 [6] Jensen, J. R. (2005). “Introductory Digital Image Processing: A Remote Sensing Perspective” Pearson Prentice Hall, New Jersey. Journal of Geoscience and Environment Protection, Vol.5 No.3 [7] Nooka Ratnam Kinthada, Murali Krishna Gurram (Feb. 2014), “Land Use/Land Cover and NDVI Analysis for Monitoring the Health of Micro-watersheds of Sarada River Basin, Visakhapatnam District, India.” Journal of Geology & Geophysics

Dam is an important way of water-resources utilization in large rivers. Dam construction has played significant roles in flood control, irrigation, navigation, and energy supply; however, the enormous negative effects, such as landslides, ecological problems, and water quality decline, could surpass positive gains. In these days, the water has become the most important need of everyone. In the monsoon season, there is over amount of water. Because of no facility of storage of water, this amount of water is being wasted. On the other side, in the summer season, there is no water for daily use only. So, our main concept for this project is to give better storage system of water to all villagers and to keep water in storage & use it in every season. The present study motives to design a dam proposed on Wakal river basin. The suitable dam site & its healthy catchment is very helpful in putting the reservoir. Extensive experiments of rock fragments and soil practical’s have been done, major survey’s on seven possible dam sites were carried out, testing have been followed by laboratory analysis. It was observed that the dam site has rocky foundation which is suitable & gravity dam may be constructed. To control flood, Gravity dam may be preferred. Various other observation will be carried out in future in addition the perform tests. Analysis of flood magnitude has been done using rational method, Empirical formulas, and Geological surveys & by software various analysis has been done. Proposal of constructing dam on Wakal river basin has been checked and it has been found that gravity concrete dam may be proposed to construct at the basin.

Keywords : Dam, Wakal River Basin, Flood Control, Structural Measures

Recent

[1] Jai Krishna, Shamsher Prakash and S.K. Thakkar, “A study of earth dam models under shock loading” 4 Vol 3 A5. [2] Brooke McDonald-Wilmsen, Michael Webber “Dams and Displacement: Raising the Standards and Broadening the Research Agenda”, Water Alternatives 3(2): 142-161, Volume 3 Issue 2. [3] Proposal on “Building Check-Dams for Drinking Water: A Teaching and Research Initiative” [4] Jhimli Bhattacharjee, “Dams and Environment Movements: The cases from India’s North East” , International Journal of Scientific and Research Publications, Volume 3, Issue 11, November 2013 1 ISSN 2250-3153. [5] Liqa-Sally Raschid, Richard Twum Koranteng and Edmund Kyei Akoto – Danso “RESEARCH, DEVELOPMENT AND CAPACITY BUILDING FOR THE SUSTAINABILITY OF DAM DEVELOPMENT WITH SPECIAL REFERENCE TO THE BUI DAM PROJECT” [6] WCD, 2000. “Dams and Development: A new framework for decision making”. The report of the World Commission on Dams. Earth scan Publications Ltd. London [7] Report of a Workshop on “Dam Safety Research Needs” Hosted by the Association of State Dam Safety Officials April 11, 1999 Washington D.C. [8] Proceedings of Workshop on “Hydrological Research Needs for Dam Safety”, US Army Corps, Hydrologic Engineering Centre.

The analysis of climate patterns can be performed separately for each climatic variable or the data can be aggregated by using a climate classification. These classifications are usually done on the basis of different data like Temperature, Rainfall and Evaporation. Thus, climatic classifications also represent a convenient tool for the validation of climate models and for the analysis of simulated future climate changes. We focus on definitions of climate types according to the Köppen-Trewartha climate classification with their different group of climates. The climate classification is carried out for the study area of Vadodara Bharuch Narmada region of Gujarat.

Keywords : Climatic Classifications, Data Collection, Rainfall Data, Temperature Data, Evaporation Data

Recent

[1] Baker B, Diaz H, Hargrove W, Hoffman F (2010) Use of the Köppen-Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People’s Republic of China. Clim Change 98: 113−131 [2] Bailey RG (2009) Ecosystem geography: from ecoregions to sites, 2nd edn. Springer, New York, NY [3] Geiger R (1954) Klassifikationen der Klimate nach W. Köppen. In: Landolf-Börnstein: Zahlenwerte und Funktionen aus Physik, Chemie, Astronomie, Geophysik und Technik, (alte Series), Vol. 3. Springer, Berlin, p 603−607 [4] Trewartha GT, Horn LH (1980) Introduction to climate, 5th edn. McGraw Hill, New York, NY [5] Köppen W (1923) Die Klimate der Erde. Grundriss der Klimakunde. Walter de Gruyter, Berlin [6] Köppen W (1931) Grundriss der Klimakunde. Walter de Gruyter, Berlin [7] Köppen W (1936) Das geographische System der Klimate. In: Köppen W, Geiger R (eds) Handbuch der Klimato - logie. Gebrüder Borntraeger, Berlin.

The Una is in Agro climatic zone VI (South Saurashtra) in Gujarat. The monthly, seasonal, dry and wet seasonal and annual trends for rainfall were analyzed for the period of 1974 to 2017 (44 year). A powerful indicator of the temporal distribution of rainfall is Precipitation Concentration Index (PCI) used for analysis of monthly, seasonal and annual rainfall data. Precipitation Concentration Index is a statistically derived index used in quantifying the relative distribution of the rainfall patterns. Rainfall seasonality is related to the temporal distribution of rainfall on a monthly basis. The normal precipitation near the study area in the basin is highest going up to 2034 mm in 2005 and lowest going up to 239 mm in 1987, with an inter annual variation of 11%. The normal annual rainfall in the study area is 935 mm. About 90%, 50%, 5.2% rainfall is received during the monsoon months of July, August and September respectively. The rainfall analysis over Una (1974 to 2017) implies a decreasing trend in southwest monsoon, while the post and pre monsoon rainfall have increasing trends. This study indicates that the annual rainfall over Una is concentrated roughly in one third of the year or in other words total rainfall occurred in four months and shows strongly irregular rainfall distribution.

Keywords : Climate, Precipitation Concentration Index, Seasonality Index

Recent

[1] Becker, S., Gemmer, M., & Jiang, T. (2006). Spatiotemporal analysis of precipitation trends in the Yangtze River catchment. Stochastic Environmental Research and Risk Assessment, 20(6), 435-444. [2] Luis, M. D., Gonzalez-Hidalgo, J. C., Brunetti, M., & Longares, L. A. (2011). Precipitation concentration changes in Spain 1946–2005. Natural Hazards and Earth System Sciences, 11(5), 1259-1265. [3] Nandargi, S. S., & Aman, K. (2018). Precipitation concentration changes over India during 1951 to 2015. Scientific Research and Essays, 13(3), 14-26. [4] Rasel, A. H., Islam, M. M., & Keramat, M. Analysis of Annual and Seasonal Precipitation Concentration Index of North-Western Region of Bangladesh. [5] Thomas, J., & Prasannakumar, V. (2016). Temporal analysis of rainfall (1871–2012) and drought characteristics over a tropical monsoon-dominated State (Kerala) of India. Journal of Hydrology, 534, 266-280. [6] Valli, M., Shanti Sree, K., & Murali Krishna, I. V. (2013). Analysis of precipitation concentration index and rainfall prediction in various agro-climatic zones of Andhra Pradesh, India. Int Res J Environ Sci, 2(5), 53-61.

In this paper seismic control problem is discussed for an extra-large Liquefied Natural Gas (LNG) storage tank using smart base-isolation. The seismic forces are reduced by isolating LNG tank from ground through laminated isolation bearing made from natural rubber. Magneto-rheological (MR) dampers are installed to control the excessive displacement of LNG tank system. The MR dampers are commended by state feedback controller designed using pole placement method. The efficacy and effectiveness of derived control algorithm are presented and compared with uncontrolled system for past three earthquake ground motion. The simulation results showed that the state feedback control strategy is more effective in reducing the structural responses as compared to uncontrolled system.

Keywords : State Feedback Control, Magneto - Rheological Damper, Pole Placement Method

Recent

[1] Dunkerley S. (1894), “On the Whirling and Vibration of Shaft.” Philosophical Transactions of the Royal Society of London, 185: 279-360. [2] Malhotra, P.K., Wenk T., and Wieland M. (2000), “Simple Procedure for Seismic Analysis of LiquidStorage Tanks.”Structural Engineering International, [3] Jansen, M., and Dyke, J. (2000), “Semi-active Control Strategies for MR Dampers: Comparative Study.”Journal of Engineering Mechanics, ASCE, Vol.126, no.8 [4] Iemura, H., Igarashi A., and Kalantari A (2004),. “Enhancing Dynamic Performance of Liquid Storage Tanks by Semi-Active Controlled Dampers.” 13th World Conference on Earthquake Engineering, Paper No. 773. [5] Jin, B., Jeon, S., Kim, S., Kim, Y., and Chung C.( 2004),“Earthquake Response Analysis Of LNG Storage Tank By Axisymmetric Finite Element Model And Comparison To The Results Of The Simple Model.” 13th World Conference on Earthquake Engineering, Paper No. 394. [6] Douglas, H., Rotzer, J., and Maurer, H. (2005), “Hazard and Safety Investigations for LNG Tanks.” LNG Journal, pp 23-24. [7] Dotoli, R., Lisi, D., and Bardaro, D. (2007). “Sloshing Response Of LNG Storage Tank Subjected To Seismic Loading.” 6th European LS-DYNA Users’ Conference. [8] Lee, K., Kim, J., and Seo, H. (2010), “Seismic Response of LNG Storage Tank under Different Base Conditions and Liquid Height.” The International Society of Offshore and Polar Engineers (ISOPE), ISBN 978. [9] Bharti, S. D., Dumne, S. M., and Shrimali, M. K. (2010), “Seismic response analysis of adjacent buildings connected with MR dampers.” Engineering Structures 32, pp. 2122 – 2133. [10] Ruifu, Z., Dagen, W., and Xiaosong, R. (2011), “Seismic analysis of a LNG storage tank isolated by a multiple friction pendulum system.” Earthquake Engineering and Engineering Vibration, Vol.10, No.2, pp. 253-262. [11] Dipesh H. Shah, Krupa D. Narwekar, (2013)”Implementing State Feedback Controller on Three-Tank Mixing Process” Journal of Control & Instrumentation, Volume 4, Issue 3, ISSN: 2229-6972. [12] Dipesh H. Shah (2013),”Modeling and Design of State Feedback with Integral Controller for TRMS (Twin-Rotor MIMO System)”, Journal of Control & Instrumentation, Volume 4, Issue 3, ISSN: 2229-6972. [13] Chowdhury Andalib Bin Karim, Muhammad Ahsan Zamee, (2014)” Design and Analysis of Pole-Placement Controller for Dynamic Stability Improvement of VSC-HVDC based Power System” The 9th International Forum on Strategic Technology (IFOST), October 21-23, Cox’s Bazar, Bangladesh. [14] Yan Lan, Fei Minrui(2011),”Design of State-feedback Controller by Pole Placement for a Coupled Set of Inverted Pendulums”, The Tenth International Conference on Electronic Measurement & Instruments, 978-1-4244-8161-3.