Bai Bing, Dong Fei, Peng Wen-Qi, Liu Xiao-Bo
China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China.
Huan Jing Ke Xue. 2025 Aug 8;46(8):5103-5111. doi: 10.13227/j.hjkx.202407244.
To deeply analyze the causal relationships among various water quality indicators in the Middle Route of South-to-North Water Diversion Project and achieve high-precision predictions, a method combining empirical dynamic modeling （EDM） and deep learning is proposed. Empirical dynamic modeling is utilized to conduct causal analysis among water quality indicators. Based on this, a dataset is constructed to train long short-term memory （LSTM） neural networks for water quality prediction. The prediction accuracy and computational time of different LSTM structures are compared. The results showed that： ① The water quality of the Middle Route of South-to-North Water Diversion was stable, with no significant abrupt changes along the route. ② There was a bidirectional causal relationship between total nitrogen and dissolved oxygen, as well as pH, in the Middle Route of South-to-North Water Diversion Project. ③ The neural network trained based on causal analysis results could achieve high-precision water quality predictions for the Middle Route of South-to-North Water Diversion Project, with the Nash efficiency coefficient of the predictions generally exceeding 0.85. This method can deeply analyze the causal relationships among variables and achieve high-precision predictions, providing scientific support for water quality management and subsequent analysis and prediction of water ecological factors in the Middle Route of South-to-North Water Diversion Project.
为深入分析南水北调中线工程各水质指标之间的因果关系并实现高精度预测，提出一种将经验动态建模（EDM）与深度学习相结合的方法。利用经验动态建模对水质指标进行因果分析。在此基础上，构建数据集来训练长短期记忆（LSTM）神经网络进行水质预测。比较了不同LSTM结构的预测精度和计算时间。结果表明：①南水北调中线水质稳定，沿线无明显突变。②南水北调中线工程中总氮与溶解氧以及pH之间存在双向因果关系。③基于因果分析结果训练的神经网络能够对南水北调中线工程实现高精度水质预测，预测的纳什效率系数一般超过0.85。该方法能够深入分析变量间的因果关系并实现高精度预测，为南水北调中线工程水质管理及后续水生态因子分析与预测提供科学支撑。
