An ANN model for estimation of potential evaporation

摘要

A back propagation artificial neural network (ANN) Model is introduced for estimation of potential evaporation from free water surface of a lake in Hoshangabad district located in a semi-arid region of India, and the results are compared with the conceptual Penman, Kohler, and Van-Bavel-Businger models and a multi-regression model. This has a three-layered network with the number of nodes in the hidden layer approximately twice the input nodes and one node in the output layer. For application, the available data were normalized by the maximum value of the variable. The learning parameters (learning rate, and momentum term) were found to exhibit a decreasing trend with increasing number of iterations. In the estimation of the potential evaporation, the Kohler method performed better than both the Penman and Van-Bavel methods for all values of pan coefficients taken as 0.6, 0.7, and 0.8, the Kohler method performed the best for a pan coefficient value equal to 0.7, and the multi-input regression model was superior to the Kohler method. Based on the criteria (Nash and Sutcliffe, 1970) of mean absolute deviation (MAD), mean square error (MSE), correlation coefficient (CC), coefficient of efficiency (CE), and volumetric efficiency (EV), the ANN model performed better than the Kohler and regression methods.