Ahmad et al. (2026) Fuzzy Modeling Strategies for Groundwater Level Forecasting: Comparing Local, Integrated, and Behavioral Frameworks for a Data-Limited Coastal Aquifer in the Eastern Mediterranean

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Identification

• Journal: Water
• Year: 2026
• Date: 2026-02-27
• Authors: Mahmoud Ahmad, Katalin Bene, Richard Ray
• DOI: 10.3390/w18050566

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study comparatively analyzes three fuzzy expert system strategies for monthly groundwater level forecasting in the semi-arid Al-Hsain Basin, Syria, finding that an innovative behavioral clustering approach significantly outperforms localized and unified models in terms of directional classification accuracy and model efficiency.

Objective

• To conduct a comprehensive comparative analysis of three fuzzy expert system strategies (localized, unified, and behavioral clustering) for monthly groundwater level forecasting in the Al-Hsain Basin, Syria, focusing on their directional classification accuracy.
• To isolate the effect of fuzzy system organization logic on forecasting performance, robustness, and transferability under an identical inference and time-series validation framework.

Study Configuration

• Spatial Scale: Al-Hsain Basin, Syria, utilizing data from 35 monitoring wells.
• Temporal Scale: Monthly groundwater level forecasting, based on four years of data (2020–2024).

Methodology and Data

• Models used: Three fuzzy expert system strategies: localized models based on hydrogeographical grouping, a unified basin-wide approach, and a behavioral clustering methodology. These systems employed fuzzy inference for directional classification (rise, stable, decline).
• Data sources: Synchronized rainfall and temperature data collected from 35 monitoring wells.

Main Results

• The behavioral clustering approach achieved the highest overall performance with a mean accuracy of 0.74.
• Localized models showed a mean accuracy of 0.71, while unified models achieved 0.67.
• Behavioral clustering was effective in 66% of wells, with individual accuracy improvements reaching up to 0.23, and reduced model complexity from five group-specific systems to three behaviorally coherent clusters.
• Localized models performed optimally in 29% of wells where hydrogeological conditions aligned with spatial assumptions.
• Unified models provided consistent moderate performance across 89% of locations.
• The incorporation of lagged variables and seasonal indices was crucial for capturing temporal complexity in semi-arid groundwater responses within behavioral clustering models.
• Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) compared to geographical groupings (0.08–0.14), indicating improved functional homogeneity.

Contributions

• This study uniquely isolates and evaluates the effect of different fuzzy system organization logics (localized, unified, and behavioral clustering) on groundwater forecasting performance, robustness, and transferability.
• It demonstrates that behavioral clustering offers an effective balance between accuracy, interpretability, and generalization, providing a novel and superior strategy for regional groundwater management in data-limited, semi-arid regions.

Funding

Not explicitly stated in the provided text.

Citation

@article{Ahmad2026Fuzzy,
  author = {Ahmad, Mahmoud and Bene, Katalin and Ray, Richard},
  title = {Fuzzy Modeling Strategies for Groundwater Level Forecasting: Comparing Local, Integrated, and Behavioral Frameworks for a Data-Limited Coastal Aquifer in the Eastern Mediterranean},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18050566},
  url = {https://doi.org/10.3390/w18050566}
}