Evaluation of soft computing and regression-based techniques for the estimation of evaporation

Abstract The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.