Application of Unsupervised Learning Algorithms in Environmental Factor Correlation Analysis and Model Construction

As environmental protection issues become more serious, scientists and policy makers are in urgent need of efficient tools to analyze and manage environmental data. This study explores the application of unsupervised learning algorithms in correlation analysis and model building, with a special focus on the effectiveness of clustering, dimensionality reduction and density estimation algorithms in processing and interpreting complex environmental data. By applying K-mean clustering, Principal Component Analysis (PCA), self-organizing mapping, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), we identified air and water pollution sources, assessed the environmental quality of multidimensional environmental data, monitored the health of ecosystems, and evaluated environmental risk areas. Higher concentrations of PM2.5 (2.5-micrometer Particulate Matter), NO2, and SO2 in the industrial areas (monitoring points 1, 3, 5, 7, and 10) indicate that there are serious air pollution problems in these areas. The results show that the unsupervised learning algorithm can effectively extract key information from environmental monitoring data to support the formulation and implementation of environmental policies. This study not only extends the application of unsupervised learning in the field of environmental sciences, but also provides a reliable data-driven method for environmental management, which helps to improve the science and effectiveness of environmental protection and sustainable development.