Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models

Estimation of pan-evaporation has a vital importance in water resources planning and management especially in arid/semi-arid regions. In this study, four heuristic approaches, multi-layer perceptron neural network (MLPNN), co-active neuro-fuzzy inference system (CANFIS), radial basis neural network (RBNN) and self-organizing map neural network (SOMNN) were utilized to estimate monthly pan-evaporation (EPm) at two locations, Pantnagar and Ranichauri, in the foothills of Indian central Himalayas. The monthly climatic data, minimum and maximum air temperatures, relative humidity in the morning and afternoon, wind speed, sun-shine hours and pan-evaporation, were used for model calibration and validation. The combination of appropriate input variables for the applied models was decided using gamma test. The results obtained by MLPNN, CANFIS, RBNN and SOMNN models were compared with climate-based empirical models, such as Stephens-Stewart (SS) and Griffith's (G), on the basis of root mean squared error, coefficient of efficiency and coefficient of correlation. The results indicated that the performance of CANFIS (RMSE = 0.627 mm, COE = 0.936, COC = 0.979) and MLPNN (RMSE = 0.214 mm, COE = 0.989, COC = 0.970) models with six input variables was superior than the others models in estimating monthly pan evaporation at Pantnagar and Ranichauri stations.