Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting

Djerbouai Salim (), Souag-Gamane Doudja, Ferhati Ahmed, Djoukbala Omar, Dougha Mostafa, Benselama Oussama and Hasbaia Mahmoud
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Djerbouai Salim: University of M’sila, Ichebila
Souag-Gamane Doudja: University of Science and Technology Houari Boumediene
Ferhati Ahmed: University of M’sila, Ichebila
Djoukbala Omar: University of M’sila, Ichebila
Dougha Mostafa: University of M’sila, Ichebila
Benselama Oussama: University of M’sila, Ichebila
Hasbaia Mahmoud: University of M’sila, Ichebila

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 3, No 19, 1420 pages

Abstract: Abstract Recently, coupled Wavelet transform and Neural Networks models (WANN) were extensively used in hydrological drought forecasting, which is an important task in drought risk management. Wavelet transforms make forecasting model more accurate, by extracting information from several levels of resolution. The selection of an adequate mother wavelet and optimum decomposition level play an important role for successful implementation of wavelet neural network based hydrologic forecasting models. The main objective of this research is to look into the effects of various discrete wavelet families and the level of decomposition on the performance of WANN drought forecasting models that are developed for forecast drought in the Algerois catchment for long lead time. The Standard Precipitation Index (SPI) is used as a drought measuring parameter at three-, six- and twelve-month scales. Suggested WANN models are tested using 39 discrete mother wavelets derived from five families including Haar, Daubechies, Symlets, Coiflets and the discrete approximation of Meyer. Drought is forecasted by the best model for various lead times varying from 1-month lead time to the maximum forecast lead time. The obtained results were evaluated using three performance criteria (NSE, RMSE and MAE). The results show that WANN models with discrete approximation of Meyer have the best forecast performance. The maximum forecast lead times are 36-month for SPI-12, 18-month for SPI-6 and 7- month for the SPI-3. Drought forecasting for long lead times have significant values in drought risk and water resources management.

Keywords: Algerois catchment; Drought; Forecasting; Neural networks; SPI; Wavelet transforms (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03432-0

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