Recent Advances in the Application of Machine Learning in Environmental Analysis and Detection
With the rapid increase in environmental data,machine learning has become an essential tool for environmental analysis and detection. The main goal of this review is to foster the continuous innovation of this field. In this review,the basic concepts of machine learning,common algorithms and software platforms are briefly introduced. Through a comprehensive literature analysis,it high-lights the latest progress in environmental analysis and detection research that incorporates machine learning technology. Currently,research on environmental analysis and detection based on machine learning methods mainly focuses on five areas:detection of conventional environmental quality indi-cators,targeted and non-targeted environmental detection analysis,classification and identification of microplastics and nanoplastics,environmental behaviour of pollutants,and source apportion-ment. Furthermore,the paper identifies several existing deficiencies in the field. These include sam-ple collection and pre-treatment,integration of new technologies,multimodal data fusion,and the interpretability and credibility of models in the current research. Finally,this review proposes poten-tial future applications of machine learning methods in environmental analysis and detection,aiming to further advance and expand the application of machine learning in this field. This review can pro-vide guidance and recommendations for researchers and decision-makers in the field of environmental analysis and detection and its intersecting areas.
万方数据
维普
