Application of Machine learning techniques in environmental governance: A review

In recent years, based on the continuous improvement of computer hardware performance, machine learning (ML) technology has made rapid development. Due to the strong ability of ML methods to find complex functions between associated data and the low cost of human and material resources, they are increasingly favored by environmental governance practitioners. Current research has confirmed that the application of ML technology to the field of environmental governance is of great significance in overcoming the difficulties of analysis and practice in traditional environmental work, and can greatly promote the long-term development of such directions as air quality prediction, solid waste classification, pollutant distribution mapping, and intelligent management of water environment. Therefore, by organizing the representative literature in the past five years, this paper describes the current status of the application of ML technology in four different elements of environmental governance, namely, atmosphere, water, soil, and solid waste, and aims to summarize the possible directions of the future development of ML technology and the new development brought to the field of environmental governance by exploring the basic concepts of ML, the advantages and disadvantages of the model and the constraints and other issues.