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.

This study assessed the ability of two models, Local Linear Regression (LLR) and Artificial Neural Network (ANN) to estimate monthly potential evaporation from Pantagar, US Nagar (India) which falls under sub-humid and subtropical climatic zone. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation have been used to train and test the developed models. A comparison was made between the estimates provided by the LLR model and ANN model. Results shown that the models were able to well learn the events they were trained to recognize. For ANN model the correlation coefficient for training period is 0.9311 and for testing period is 0.9236 and the value of root mean square error for training period is 1.070 and for testing period it is 0.9863. In case of LLR model the correlation coefficient for training period is 0.9746 and for testing period is 0.9273 and value of root mean square error for training period is 0.6121 and for testing period it is 1.5301.