Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 8, Issue 6
Abstract
Droughts are destructive climatic extreme events that may cause significant damage both in natural environments and in human lives. Drought forecasting plays an important role in the control and management of water resources systems. In this study, a conjunction model is presented to forecast droughts. The proposed conjunction model is based on dyadic wavelet transforms and neural networks. Neural networks have shown great ability in modeling and forecasting nonlinear and nonstationary time series in a water resources engineering, and wavelet transforms provide useful decompositions of an original time series. The wavelet-transformed data aid in improving the model performance by capturing helpful information on various resolution levels. Neural networks are used to forecast decomposed subsignals in various resolution levels and reconstruct forecasted subsignals. The model was applied to forecast droughts in the Conchos River Basin in Mexico, which is the most important tributary of the Lower Rio Grande/Bravo. The performance of the conjunction model was measured using various forecast skill criteria. The results indicate that the conjunction model significantly improves the ability of neural networks to forecast the indexed regional drought.
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Information
Published In
Journal of Hydrologic Engineering
Volume 8 • Issue 6 • November 2003
Pages: 319 - 328
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Nov 15, 2002
Accepted: May 27, 2003
Published online: Oct 15, 2003
Published in print: Nov 2003
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