| 机器学习在人工湿地水质净化领域的研究进展 |
| 摘要点击 571 全文点击 43 投稿时间：2024-12-19 修订日期：2025-03-19 |
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| 中文关键词 机器学习 人工湿地 水质净化 数据收集 模型构建 |
| 英文关键词 machine learning constructed wetland water purification data collection model building |
| DOI 10.13227/j.hjkx.20260239 |
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| 中文摘要 |
| 随着环境科学与计算机技术的快速发展，机器学习在人工湿地水质净化研究中的应用日益广泛. 综述了近年来机器学习在人工湿地水质净化领域的主要研究进展，对机器学习算法在人工湿地应用方向和人工湿地数据特点进行了全面详细分析；进一步从机器学习解析人工湿地的水质净化关键过程（微生物代谢与污染物降解、植物吸收与转化过程、基质吸附与过滤机制和水力条件），多维度数据融合与水净化机制重构和机器学习在优化人工湿地设计与运行中的应用这3个方面展开了分析，并对未来的研究方向从模型改进与创新、数据融合与共享、实时监测与智能控制、与其他技术集成和环境影响评估与生态修复这5个方面进行了展望. 结果表明，机器学习为人工湿地水质净化研究提供了新的视角和方法，有助于提高水质净化效率和管理水平，但仍需进一步优化算法和解决实际应用中的问题. |
| 英文摘要 |
| In the current era， the rapid development of environmental science and computer technology complement each other. Among them， the application of machine learning in the research field of water quality purification in constructed wetlands has become increasingly prominent， showing vigorous vitality and broad prospects. This paper focuses on this aspect and systematically reviews the main research progress of machine learning in water quality purification in constructed wetlands in recent years， striving to comprehensively and deeply analyze its key threads. On the one hand， an in-depth exploration of the diverse application directions of machine learning algorithms in constructed wetlands is carried out， covering key links in water quality purification such as microbial metabolism and pollutant degradation， plant absorption and transformation processes， substrate adsorption and filtration mechanisms， and hydraulic conditions， accurately grasping the adaptation patterns of algorithms in each link. At the same time， the characteristics of constructed wetland data are deeply excavated to lay a solid foundation for algorithm optimization. Further， it is elaborated from three core dimensions， in which we： ① focus on the key processes of water purification assisted by machine learning and clarify the complex interaction mechanisms among microorganisms， plants， substrates， and hydraulic conditions； ② commit to multi-dimensional data fusion to reshape the architecture of water purification mechanisms and drive in-depth understanding with data； and ③ explore the practical applications of machine learning in the design optimization and operation control of constructed wetlands to enhance wetland efficiency. In addition， the article takes a long-term view and looks ahead to future research directions from five aspects， namely model improvement and innovation， data fusion and sharing， real-time monitoring and intelligent control， integration with other technologies， and environmental impact assessment and ecological restoration. To summarize， machine learning injects new vitality into the research on water quality purification in constructed wetlands， opens up new perspectives， and effectively promotes the efficiency of water quality purification and management levels. However， at present， continuous efforts still need to be made in the in-depth optimization of algorithms and solving practical application problems to fully release its potential. |
