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Water quality prediction is essential for effective water resource management and pollution prevention. In China, research on predictive analytics for various water bodies has not kept pace with environmental needs. This study addresses this gap by conducting a comprehensive analysis and modeling of water quality monitoring data from multiple distributed water bodies specifically within the Yangtze River Delta. Using a novel approach, this paper introduces a distributed water quality prediction system enhanced by a CNN-LSTM joint model. This model synergistically combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks to robustly extract and utilize spatiotemporal data, thereby significantly improving the accuracy of predicting dynamic water quality trends. Notably, the excellent predictive performance of the joint model enables its prediction results to achieve RMSE and MAPE as low as 1.08% and 6.8%, respectively. Empirical results from this study highlight the system’s superior predictive performance. Based on these findings, this paper offers targeted recommendations for water quality monitoring, treatment, and management strategies tailored to the specific needs of the Yangtze River Delta. These contributions are poised to aid policymakers and environmental managers in making more informed decisions.
Wu et al. (Sat,) studied this question.