ArticleScientific reports2026
Prediction of groundwater level in shallow and complex aquifers using hybrid soft computing models and metaheuristic algorithms.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 m
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