Abstract
The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.
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This work was financially supported by the Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07101003), National Key Research and Development Project (2019YFC1804800), the Science and Technology Program of Guangdong Forestry Administration (2020-KYXM-08), Pearl River S&T Nova Program of Guangzhou, China (No. 201710010065), Youth Foundation of SCIES (PM-zx097-202304-147), and European Social Fund via IT Academy Program.
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Xiaohua Fu: initial idea and conceptualization, investigation, and visualization. Jie Jiang: methodology, writing—original draft, and writing—review and editing. Xie Wu: methodology and formal analysis. Lei Huang: resources, formal analysis, writing—review and editing, and conceptualization. Rui Han: reviewing and writing—review and editing. Kun Li: reviewing and writing—review and editing. Chang Liu: reviewing and writing—review and editing. Nesma Talaat Abbas Mahmoud: data curation, software, reviewing, and writing—literature review. Jianyu Chen: reviewing and writing—review and editing. Zhenxing Wang: supervision, funding acquisition, project administration, and review and editing. Kallol Roy: conceptualization, validation, review and editing, and supervision.
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Fu, X., Jiang, J., Wu, X. et al. Deep learning in water protection of resources, environment, and ecology: achievement and challenges. Environ Sci Pollut Res 31, 14503–14536 (2024). https://doi.org/10.1007/s11356-024-31963-5
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DOI: https://doi.org/10.1007/s11356-024-31963-5