Modeling monthly evaporation using two different neural computing techniques

Two different artificial neural network (ANN) techniques, multi-layer perceptrons (MLP) and radial basis neural networks (RBNN), are employed in the estimation of monthly pan evaporation. The monthly climatic data, air temperature, solar radiation, wind speed, pressure and humidity, of three stations operated by the U.S. Environmental Protection Agency in California are used as inputs to the ANN models to estimate monthly evaporation. In the first part of the study, the accuracy of MLP and RBNN are compared with each other. The multi-linear regression (MLR) and the Stephens–Stewart (SS) methods are also considered for the comparison. The concern of second part of the study is to investigate the ability of neural computing techniques, MLR and SS models in evaporation estimation using data from nearby station(s). The performances of the models are evaluated using mean square errors, mean absolute relative errors and determination coefficient (R2). The effect of periodicity on model’s estimation performance is also investigated. Based on the comparisons, it is found that the MLP and RBNN computing techniques could be employed successfully in modeling monthly evaporation process.