[Research Progress of Machine Learning in the Field of Constructed Wetland Water Purification]

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.