Synergistic identification of hydrogeological parameters and pollution source information for groundwater point and areal source contamination based on machine learning surrogate–artificial hummingbird algorithm

Luo, Chengming; Wang, Xihua; Xu, Y. Jun; Jia, Shunqing; Liu, Zejun; Mao, Boyang; Lv, Qinya; Ji, Xuming; Rong, Yanxin; Dai, Yan

doi:https://doi.org/10.5194/hess-29-5719-2025

Articles | Volume 29, issue 20

https://doi.org/10.5194/hess-29-5719-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-29-5719-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 29, issue 20

Research article

|

24 Oct 2025

Research article |

| 24 Oct 2025

Synergistic identification of hydrogeological parameters and pollution source information for groundwater point and areal source contamination based on machine learning surrogate–artificial hummingbird algorithm

Chengming Luo, Xihua Wang, Y. Jun Xu, Shunqing Jia, Zejun Liu, Boyang Mao, Qinya Lv, Xuming Ji, Yanxin Rong, and Yan Dai

Supplement

https://doi.org/10.5194/hess-29-5719-2025-supplement

Data sets

PSC data ming https://doi.org/10.5281/zenodo.17386323

Model code and software

Surrogate model-optimization algorithm L. Chengming https://doi.org/10.5281/zenodo.14568110

Short summary

This study constructed a backpropagation neural network surrogate–artificial hummingbird algorithm inversion framework to accurately and synergistically identify the pollution source information and hydrogeological parameters, which provided a reliable basis for groundwater contamination remediation and management.