Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks

TLDR

Droughts are destructive climatic extremes that threaten natural environments and human life, and accurate forecasting is essential for water resource management, with neural networks and wavelet transforms offering powerful tools for modeling nonlinear, nonstationary time series. The study presents a conjunction model to forecast droughts. The model combines dyadic wavelet transforms with neural networks, using wavelet‑transformed subsignals to train networks that forecast and reconstruct drought indices, and was applied to the Conchos River Basin in Mexico with performance evaluated by forecast skill metrics. The conjunction model significantly enhances neural network drought forecasting accuracy for the regional index.

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.