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<p>Predicting groundwater level dynamics in shallow, unconfined aquifers remain a persistent challenge due to their high sensitivity to heterogeneous recharge, rapid anthropogenic extraction, and nonlinear interactions with climatic drivers conditions under which conventional models often fail to generalize. Here, we introduce two novel hybrid frameworks ANN-HBO and ANN-POA that couple artificial neural networks with metaheuristic optimization algorithms (Honey Badger Optimization and Pelican Optimization) to dynamically optimize network weights in data-scarce, non-stationary environments where deep learning architectures exhibit poor convergence and overfitting. The study is conducted in the Astaneh-Kuchesfahan aquifer, northern Iran, a shallow alluvial system limited storage capacity, and strong seasonal fluctuations driven by irrigation demand and evaporation. Hydraulic conductivity varies spatially from 10 to 35&#xa0;m/day, reflecting moderate to high transmissivity (approximately 200–600 m<sup>2</sup>/day), typical of well-drained fluvial systems. Using 256&#xa0;months of hydro-meteorological records, we identify the most informative predictors lagged groundwater levels (1–3&#xa0;months), temperature, precipitation, evaporation, and abstraction rates via the Minimum Redundancy Maximum Relevance (MRMR) algorithm, generating four optimized input scenarios. Compared to a standalone ANN, ANN-POA reduces RMSE by 29.3% and MAE by 24.1% on average across three observation wells, achieving test RMSE values of 0.213–0.287&#xa0;m and MAE of 0.260–0.437&#xa0;m. Lagged groundwater level (1-month) and monthly precipitation are identified as the dominant predictors, confirming the system’s strong dependence on recent recharge. The superior robustness of ANN-POA over ANN-HBO and baseline models demonstrates its efficacy in navigating complex, high-dimensional parameter spaces without requiring extensive training data. This approach enables water managers in data-limited regions to transition from reactive to anticipatory aquifer management: identifying early signs of depletion, prioritizing managed recharge, and calibrating extraction quotas based on dynamic hydrological responses rather than static averages. Our framework offers a scalable, physics-informed, and low-cost solution for sustainable groundwater governance in vulnerable shallow aquifers worldwide.</p>

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Prediction of groundwater level in shallow and complex aquifers using hybrid soft computing models and metaheuristic algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月24日

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  • Saeed Mohammadpour,
  • Arash Ebrahimabadi,
  • Hamidreza Rabiefar

摘要

Predicting groundwater level dynamics in shallow, unconfined aquifers remain a persistent challenge due to their high sensitivity to heterogeneous recharge, rapid anthropogenic extraction, and nonlinear interactions with climatic drivers conditions under which conventional models often fail to generalize. Here, we introduce two novel hybrid frameworks ANN-HBO and ANN-POA that couple artificial neural networks with metaheuristic optimization algorithms (Honey Badger Optimization and Pelican Optimization) to dynamically optimize network weights in data-scarce, non-stationary environments where deep learning architectures exhibit poor convergence and overfitting. The study is conducted in the Astaneh-Kuchesfahan aquifer, northern Iran, a shallow alluvial system limited storage capacity, and strong seasonal fluctuations driven by irrigation demand and evaporation. Hydraulic conductivity varies spatially from 10 to 35 m/day, reflecting moderate to high transmissivity (approximately 200–600 m2/day), typical of well-drained fluvial systems. Using 256 months of hydro-meteorological records, we identify the most informative predictors lagged groundwater levels (1–3 months), temperature, precipitation, evaporation, and abstraction rates via the Minimum Redundancy Maximum Relevance (MRMR) algorithm, generating four optimized input scenarios. Compared to a standalone ANN, ANN-POA reduces RMSE by 29.3% and MAE by 24.1% on average across three observation wells, achieving test RMSE values of 0.213–0.287 m and MAE of 0.260–0.437 m. Lagged groundwater level (1-month) and monthly precipitation are identified as the dominant predictors, confirming the system’s strong dependence on recent recharge. The superior robustness of ANN-POA over ANN-HBO and baseline models demonstrates its efficacy in navigating complex, high-dimensional parameter spaces without requiring extensive training data. This approach enables water managers in data-limited regions to transition from reactive to anticipatory aquifer management: identifying early signs of depletion, prioritizing managed recharge, and calibrating extraction quotas based on dynamic hydrological responses rather than static averages. Our framework offers a scalable, physics-informed, and low-cost solution for sustainable groundwater governance in vulnerable shallow aquifers worldwide.