Research on The Inversion Model of Water Environment Parameters of Coal Mining Subsidence Waters Based on Machine Learning

The Long-term large-scale mining of coal underground has led to the destruction of the initial water system structure on the surface, and the water pollution of subsidence waters has become increasingly serious. The accuracy of the traditional water quality parameter concentration inversion model is low, and the current improvement of water quality monitoring technology and the improvement of the inversion accuracy of water quality parameters will play a vital role in protecting the water resources in the mining area. This study focuses on coal mining subsidence water areas in Huainan City, combining measured water quality data from spring, summer, autumn, and winter of 2024 with concurrent Sentinel-2 satellite imagery. Based on statistical regression algorithms and three machine learning algorithms of Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), the concentrations of total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH₄⁺-N) and chlorophyll-a (Chl-a) in subsidence waters are fitted and modeled and the accuracy of the model is verified. A comprehensive comparison of model performance in water quality inversion revealed that machine learning models significantly outperformed traditional statistical regression models in terms of inversion accuracy. Among them, RF, DT, and SVM exhibited varying strengths across different seasons and water quality parameters, with the best-performing models achieving coefficient of determination (R²) values generally exceeding 0.8 and stable validation accuracy. These findings highlight the advantages of machine learning algorithms in water quality remote sensing inversion and further confirm the technical feasibility of this approach for monitoring complex aquatic environments. By integrating scientific data analysis with machine learning techniques, It not only provides more accurate data support for the monitoring and management of water quality in coal mining subsidence waters, but also provides a scientific decision-making basis for water ecological protection.