Data-driven identification of pollution sources and water quality prediction using Apriori and LSTM models: A case...

The rapid development of urbanization and industrialization has exacerbated surface water pollution, especially from point sources such as industrial discharge and urban wastewater, posing a severe challenge to global environmental health and sustainable development. This study combines the Apriori algorithm and Long Short-Term Memory (LSTM) networks to identify major pollution sources and predict dynamic changes in water quality. The study area encompasses four national monitoring hydrological stations in the core area of the South-to-North Water Diversion Project, with multi-source data collected, including water quality parameters and industry-specific discharge data. Using the Apriori algorithm, the pollutants with the highest support-chemical oxygen demand (COD), copper (Cu), suspended solids (SS), and zinc (Zn)-demonstrated a support value of 0.87, indicating that the metallurgical, electroplating, and chemical industries are the primary pollution sources. Further association rule analysis based on varying parameter thresholds revealed that when COD is present, the co-occurrence confidence for Cadmium (Cd), Cu, Lead (Pb), and SS reaches 0.9, and the combination of COD, Cu, Pb, SS, and Cyanide (CN) achieves a confidence level of 1, indicating a high degree of correlation among these pollutants. The LSTM model demonstrated high accuracy in water quality prediction, with Root Mean Square Error (RMSE) values for COD predictions at each hydrological station ranging from 0.2076 to 0.3366, and coefficients of determination (R²) all exceeding 0.9, highlighting the model's stability and predictive accuracy. This study provides a scientific basis for the sustainable management of watershed water resources and serves as a significant reference for environmental policymaking and water resource protection.