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<p>Groundwater drought has become a serious concern for water security in arid and semi-arid climates, largely due to the overuse of aquifers combined with climate variability and anthropogenic activities. The complex dynamics of groundwater systems, which are influenced by a wide range of climatic, geological, and anthropogenic factors, necessitate the development of accurate and reliable predictive models. This study proposes a comprehensive point and interval prediction scheme that integrates the Grey Wolf Optimizer (GWO) and Kernel Extreme Learning Machine (KELM), enhanced by multiple signal decomposition techniques, to perform multi-horizon forecasting of the Standardized Groundwater Index (SGI). To address hydrogeological heterogeneity, wells were clustered based on their drought-response behaviors using Self-Organizing Maps (SOM), and representative wells from each cluster were subsequently selected for forecasting analysis. Pre-processing with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), and Empirical Fourier Decomposition (EFD) significantly improved the input data quality by effectively isolating and extracting non-stationary features from the original time series. The point forecasting results demonstrated the consistent superiority of the VMD–GWO–KELM model, particularly for short-term horizons. Furthermore, uncertainty analysis using the Lower–Upper Bound Estimation (LUBE) framework highlighted the superiority of VMD–GWO–KELM in constructing reliable and sharp prediction intervals, with coverage probabilities approaching nominal confidence levels.</p>

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A novel implementation of a decomposition-enhanced hybrid GWO–KELM model with LUBE for constructing prediction intervals of groundwater drought

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • Saman Shahnazi,
  • Kiyoumars Roushangar,
  • Armin Farshbaf,
  • Hossein Hashemi

摘要

Groundwater drought has become a serious concern for water security in arid and semi-arid climates, largely due to the overuse of aquifers combined with climate variability and anthropogenic activities. The complex dynamics of groundwater systems, which are influenced by a wide range of climatic, geological, and anthropogenic factors, necessitate the development of accurate and reliable predictive models. This study proposes a comprehensive point and interval prediction scheme that integrates the Grey Wolf Optimizer (GWO) and Kernel Extreme Learning Machine (KELM), enhanced by multiple signal decomposition techniques, to perform multi-horizon forecasting of the Standardized Groundwater Index (SGI). To address hydrogeological heterogeneity, wells were clustered based on their drought-response behaviors using Self-Organizing Maps (SOM), and representative wells from each cluster were subsequently selected for forecasting analysis. Pre-processing with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), and Empirical Fourier Decomposition (EFD) significantly improved the input data quality by effectively isolating and extracting non-stationary features from the original time series. The point forecasting results demonstrated the consistent superiority of the VMD–GWO–KELM model, particularly for short-term horizons. Furthermore, uncertainty analysis using the Lower–Upper Bound Estimation (LUBE) framework highlighted the superiority of VMD–GWO–KELM in constructing reliable and sharp prediction intervals, with coverage probabilities approaching nominal confidence levels.