Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5719-2025
https://doi.org/10.5194/hess-29-5719-2025
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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Cited articles

Asher, M. J., Croke, B. F. W., Jakeman, A. J., and Peeters, L. J. M.: A review of surrogate models and their application to groundwater modeling, Water Resour. Res., 51, 5957–5973, https://doi.org/10.1002/2015wr016967, 2015. 
Bai, Y., Lu, W., Li, J., Chang, Z., and Wang, H.: Groundwater contamination source identification using improved differential evolution Markov chain algorithm, Environ. Sci. Pollut. Res., 29, 19679–19692, https://0.1007/s11356-021-17120-2, 2022. 
Chen, D., Lu, J., and Shen, Y.: Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non-point source polluted river in Zhejiang Province, southeast China, Hydrol. Process., 24, 290–299, https://10.1002/hyp.7482, 2010. 
Chengming, L.: Surrogate model-optimization algorithm, Zenodo [code], https://doi.org/10.5281/zenodo.14568110, 2024. 
Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., and Sindhya, K.: A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization, IEEE Trans. Evol. Comput., 22, 129–142, https://10.1109/tevc.2016.2622301, 2018. 
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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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