Archive

Publication for Volume-3 Issue-11, October 2018

Title
:
Artificial Neural Network Modeling for Monthly Evaporation
Article Type
:
Research Article
Author Name(s)
:
Aparajita singh, Research scholar, department of farm engineering, IAS, BHU, Varanasi; A. R. Senthil kumar ,National institute of hydrology, Roorkee; R. M. Singh ,Institute of agricultural sciences, BHU
Country
:
India
Research Area
:
Soil and water conservation Engineering
Most of the available researches established a linear relationship between the parameters that affects the evaporation and the target evaporation. Now it is proved that the parameters which affect the evaporation are nonlinear in nature. This leads the whole researches towards the nonlinear estimation of evaporation. The evaporation affecting parameters are nonlinear and follow a very irregular trend so an Artificial Neural Network (ANN) has evolved a better technique to establish the structural relationship between the various entities. This paper examines the applicability of ANN approach to model the monthly evaporation. The input combinations to model monthly evaporation were selected on the basis of data’s statistical properties which were obtained from NIH observatory. The highest correlation coefficient valves during calibration and validation were (0.831, 0.819) with lowest RMSE valves (0.376, 0.261) respectively for best evaporation model [4-5-1]. This was obtained with all 4 input parameters namely, monthly rainfall, monthly maximum temperature, monthly minimum temperature and monthly relative humidity at the same time. The comparison was made between the observed and computed values of evaporation which emphasizes the usefulness of ANN technique for monthly evaporation estimation.
Keywords : ANN, Evaporation, Calibration, Validation
[1]	ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, 2000. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), pp.124-137.
[2]	Anctil, F. and Tape, D.G., 2004. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. Journal of Environmental Engineering and Science, 3(S1), pp.S121-S128.
[3]	Chiew, F.H.S., Stewardson, M.J. and McMohan, T.A. 1993. Comparison of six rainfall-runoff modelling approaches. , 147: 1-36.
[4]	Dawson, C.W. and Wilby, R., 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1), pp.47-66.
[5]	Fausett, L., 1994. Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc.
[6]	Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W. and Pruitt, W.O., 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), pp.224-233.
[7]	McCulloch, W.S. and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), pp.115-133.
[8]	Maier, H.R. and Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), pp.101-124.
[9]	Sudheer, K.P., Gosain, A.K., Mohana Rangan, D. and Saheb, S.M., 2002. Modelling evaporation using an artificial neural network algorithm. Hydrological Processes, 16(16), pp.3189-3202.
[10]	Xu CY, Singh VP (2005) Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions Journal of Hydrology 308(1-4) 105-121
[11]	Zhang, Guoqiang, B. Eddy Patuwo, and Michael Y. Hu. "Forecasting with artificial neural networks: The state of the art." International journal of forecasting 14.1 (1998): 35-62.
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