EVAPORATION MODELLING BY MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK

Evaporation is one of the main elements that effect water storage and temperature in the hydrological cycle and plays an important role in evaluation of water availability. Although, there are empirical formulae available for evaporation estimation but their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. In this study, an attempt has been made to develop multiple linear regression (MLR) and artificial neural network (ANN) based evaporation estimation models using climatic parameters as inputs and evaporation as output for Udaipur of Rajasthan with the aid of Gamma test (GT). Average temperature (T), average wind speed (W), relative humidity (Rh) and sunshine hours (S) data were used as input and estimated evaporation was considered as output. The performance of the developed model was evaluated using root mean square error (RMSE) and correlation coefficient. The result showed an appropriate correlation between the estimated evaporation and actual evaporation.