Intelligent prediction of water quality plays a pivotal role in water pollution control, water resource protection, emergency decision-making for sudden water pollution incidents, tracking and evaluation of water quality changes in river basins, and is crucial to ensuring water security. The primary methodology employed in this paper for water quality prediction is as follows: (1) utilizing the comprehensive pollution index method and Mann-Kendall (MK) trend analysis method, an assessment is made of the pollution status and change trend within the basin, while simultaneously extracting the principal water quality parameters based on their respective pollution share rates; (2) employing the spearman method, an analysis is conducted to identify the influential factors impacting each key parameter; (3) subsequently, a water quality parameter prediction model, based on Long Short-Term Memory (LSTM) analysis, is constructed using the aforementioned driving factor analysis outcomes. The developed LSTM model in this study showed good prediction performance. The average coefficient of determination (R²) of the prediction of crucial water quality parameters such as total nitrogen (TN) and dissolved oxygen (DO) reached 0.82 and 0.86 respectively. Additionally, the error analysis of WQI prediction results showed that >75% of the prediction errors were in the range of 0–0.15. The comparative analysis revealed that the LSTM model outperforms both the random forest (RF) model in time series prediction and demonstrates superior robustness and applicability compared to the AutoRegressive Moving Average with eXogenous inputs model (ARMAX). Hence, the model developed in this study offers valuable technical assistance for water quality prediction and early warning systems, particularly in economically disadvantaged regions with limited monitoring capabilities. This contribution facilitates resource optimization and promotes sustainable development.

水质智能预测在水污染控制、水资源保护、突发水污染事件应急决策、流域水质变化跟踪评估等方面发挥着关键作用，对保障水安全至关重要。本文水质预测的主要方法是：（1）利用综合污染指数法和曼肯德尔（MK）趋势分析法，对流域内的污染状况和变化趋势进行评估。同时根据各自的污染份额率提取主要水质参数；(2)采用斯皮尔曼法进行分析，找出影响各关键参数的影响因素；(3)随后，利用上述驱动因素分析结果，构建基于长短期记忆(LSTM)分析的水质参数预测模型。本研究开发的 LSTM 模型表现出良好的预测性能。总氮（TN）和溶解氧（DO）等关键水质参数预测的平均决定系数（R ^{2 ）分别达到0.82和0.86。}此外，对WQI预测结果的误差分析表明，>75%的预测误差在0~0.15范围内。比较分析表明，LSTM 模型在时间序列预测方面优于随机森林 (RF) 模型，并且与具有外源输入的自回归移动平均模型 (ARMAX) 相比，表现出优越的鲁棒性和适用性。因此，本研究开发的模型为水质预测和预警系统提供了宝贵的技术帮助，特别是在监测能力有限的经济落后地区。这一贡献有助于资源优化并促进可持续发展。