Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6459-2026
https://doi.org/10.5194/hess-30-6459-2026
Research article
 | 
22 Oct 2026
Research article |  | 22 Oct 2026

A data-driven method coupling multiple physical constraints for correcting structural errors in groundwater contaminant transport models

Jinglong Tian, Xiankui Zeng, Yue Pan, Dong Wang, and Jichun Wu

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Cited articles

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Short summary
Computer models that predict how pollutants spread through groundwater often contain errors, making forecasts unreliable. To fix this, we developed a new method that combines data-driven learning with physical laws. Our approach uses multiple physical constraints simultaneously, and dynamically adjusts their importance during calculations. This works because the extra constraints act like having additional measurements, helping the model better identify key parameters and reduce uncertainty.
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