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

Author

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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)

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.

Suggested Citation

  • Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:3:d:10.1007_s11269-023-03432-0
    DOI: 10.1007/s11269-023-03432-0
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    References listed on IDEAS

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    1. Yan-Fang Sang, 2012. "A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3345-3365, September.
    2. J. Drisya & D. Sathish Kumar & Thendiyath Roshni, 2021. "Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3653-3672, March.
    3. Salim Djerbouai & Doudja Souag-Gamane, 2016. "Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2445-2464, May.
    4. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    5. Anshuka Anshuka & Floris F. van Ogtrop & R. Willem Vervoort, 2019. "Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(2), pages 955-977, June.
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    1. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.

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