Artificial Neural Network Modeling for Monthly Evaporation

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. Citation: Aparajita Singh, A. R. Senthil kumar, R. M. Singh. "Artificial Neural Network Modeling for Monthly Evaporation ." Global Research and Development Journal For Engineering 3.11 2018: 1 - 4.