Interpretable application of machine learning techniques in the assessment of coastal water quality of Poyang Lake, China

Abstract

Machine learning models (MLMs) have made substantial progress across diverse scientific domains owing to their strong predictive capabilities and effectiveness in extracting patterns from high-dimensional data. In lake environmental management, clarifying the relationships between environmental factors and water quality indicators (WQIs) is essential for improving assessment accuracy and operational efficiency. In this study, four MLMs (support vector regression [SVR], random forest [RF], extreme gradient boosting [XGBoost], and k-nearest neighbors [KNN]) were applied to identify the key environmental parameters influencing WQIs in the coastal zone of Poyang Lake, China, and to evaluate the predictive performance of each model. Model interpretability was enhanced using the SHapley Additive exPlanations (SHAP) method, which quantified the contributions of environmental variables to WQI variability. Total nitrogen (TN), total phosphorus (TP), and chlorophyll-a (Chla) concentrations were used as representative WQIs. Among all models, XGBoost consistently exhibited the highest predictive accuracy. Electrical conductivity (EC), water temperature (T), dissolved oxygen (DO), oxidation–reduction potential (ORP), and pH were identified as the five most influential parameters. Specifically, EC, T, and DO were the dominant drivers of TN and Chla, accounting for 92.8% and 84.2% of the total SHAP contributions, respectively. Meanwhile, TP was primarily governed by ORP, pH, and T, with a combined contribution of 70.6%. XGBoost accurately reproduced the observed WQIs using environmental parameters as inputs. These findings demonstrate that interpretable MLMs can effectively reveal the environmental drivers of lake water quality and offer a robust framework for ecosystem monitoring and forecasting in freshwater environments.