Prediction of groundwater level in shallow and complex aquifers using hybrid soft computing models and metaheuristic algorithms
Saeed Mohammadpour, Arash Ebrahimabadi, Hamidreza Rabiefar · 2026
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Description
Saeed Mohammadpour and colleagues forecast groundwater levels in a shallow aquifer in northern Iran, where patchy recharge, heavy pumping, and weather swings defeat standard models. Their test ground is the Astaneh-Kuchesfahan alluvial aquifer, a thin store that swings with irrigation demand and evaporation.
Two hybrid models pair neural networks with nature-inspired optimisers, honey badger and pelican methods, to tune network weights in scarce, shifting data. Hydraulic conductivity there ranges from 10 to 35 metres per day across the ground. Water managers get models built for thin aquifers where deep learning alone overfits and stalls.
Catalog record
| CREATOR | Saeed Mohammadpour, Arash Ebrahimabadi, Hamidreza Rabiefar |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 109 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |