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.
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.
the Creative Commons Attribution 4.0 License.
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
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Xihua Wang
CORRESPONDING AUTHOR
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Department of Earth and Environmental Sciences, University of Waterloo, ON N2L 3G1, Canada
Y. Jun Xu
School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, Louisiana, USA
Shunqing Jia
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Zejun Liu
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Boyang Mao
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Qinya Lv
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Xuming Ji
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Yanxin Rong
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
Yan Dai
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
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Boting Hu, Liwen Chen, Yanfeng Wu, Jingxuan Sun, Y. Jun Xu, Qingsong Zhang, and Guangxin Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-71, https://doi.org/10.5194/hess-2024-71, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This study presents a novel framework to accurately quantify wetland depression water storage capacity. The framework and its concept are transferable to other wetland areas in the world where field measurements and/or high-resolution terrain data are unavailable. Moreover, the framework provides accurate distribution and depth-area relations of wetland depressions which can be incorporated into wetland modules of hydrological models to improve the accuracy of flow and storage predictions.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
Short summary
Short summary
Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
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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.
This study constructed a backpropagation neural network surrogate–artificial hummingbird...