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Irrigation Water Demand Forecasting Using Wavelet Transforms and Artificial Intelligence

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2011 Louisville, Kentucky, August 7-10, 2011  1111803.(doi:10.13031/2013.38174)
Authors:   Jan Franklin Adamowski, Hiu Fung Chan, Inmaculada Pulido-Calvo
Keywords:   Irrigation, water demand, forecasting, wavelet transforms, artificial neural networks

Irrigation water demand forecasts are an important component of cost-effective and sustainable management and optimization of irrigation systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANN) for irrigation water demand forecasting applications is proposed. ANN and WA-ANN models for irrigation water demand forecasting were developed, and their relative performance was compared using the coefficient of determination, normalized root mean square error, and Nash Sutcliffe efficiency index. The WA-ANN models were found to provide more accurate irrigation water demand forecasts than ANN and traditional models. The results of this study indicate that coupled wavelet-neural network models are a promising new method of irrigation water demand forecasting.

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