Premium
Modelling evaporation using an artificial neural network algorithm
Author(s) -
Sudheer K. P.,
Gosain A. K.,
Mohana Rangan D.,
Saheb S. M.
Publication year - 2002
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.1096
Subject(s) - artificial neural network , evaporation , generalization , pan evaporation , data set , computer science , algorithm , set (abstract data type) , backpropagation , process (computing) , data mining , machine learning , artificial intelligence , mathematics , meteorology , mathematical analysis , physics , programming language , operating system
This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.