Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 17
Articles | Volume 29, issue 17
https://doi.org/10.5194/hess-29-4251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 17
Research article
 | 
10 Sep 2025
Research article |  | 10 Sep 2025

Enhancing inverse modeling in groundwater systems through machine learning: a comprehensive comparative study

Junjun Chen, Zhenxue Dai, Shangxian Yin, Mingkun Zhang, and Mohamad Reza Soltanian

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Short summary
Balancing exploration and exploitation in conventional inversion algorithms is challenging. We evaluated the effectiveness of tandem neural network architecture (TNNA) in addressing this challenge. Inversion problems with different heterogeneous scenarios were designed to compare TNNA with four metaheuristic algorithms. Results show that TNNA significantly improves computational efficiency and accuracy, offering a promising framework for developing robust inversion algorithms.
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