Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 20
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

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Latest update: 25 Oct 2025
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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.
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