Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013–2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.

中文翻译：

---

考虑时空效应的创新型可解释人工智能引导水质评估，对河流进行风险对抗分析

鉴于工业污染的威胁迫在眉睫，水安全仍然是一个关键问题，需要对水质进行全面评估，以应对季节性波动和影响因素，同时为决策者制定有效的策略。这项研究介绍了一种评估韩国复杂河流区域水质的新方法：汉江，包括位于南北溪流（包括庆安溪）交汇处的五条河流，最终通向八堂湖。通过利用 2013 年至 2022 年 14 个不同地点的每月水特征数据，确定了重要的季节趋势和对水质的潜在影响。这里的水质采用所提出的分指标水质指数（s-WQI）的方法计算。每个位置的 s-WQI 组合预测方法是通过一系列数据预处理方法（包括机器学习预测之前的 Hampel 过滤和特征选择）进行的。作为回报，光梯度增强 (LGB) 是最准确的预测器，其性能优于其他预测算法，尤其是通过 LGB-Pearson 和 LGB-Spearman 组合来预测南北溪流交叉点，以及 LGB-Pearson 组合来预测八堂湖。为了进一步评估该评估的稳健性并将结果扩展到可预见的场景，针对从每个考虑的位置获得的水特征分布进行了 10,000 次基于季节的蒙特卡罗模拟尝试，以确定其中的风险界限。通过 SHAP 分析进一步解释结果，通过局部和全球频谱确定每种水特征对水质的贡献。这项研究产生了实际意义，为水质增强和预警系统提供了量身定制的策略。基于人工智能的预测和特征选择的集成凸显了计算技术在推进数据驱动的水质评估、塑造环境科学研究的未来方面的变革潜力。