用混合模型预测全球水质，精准率达99%，还配了可解释的聊天助手。
AI for Water Sustainability: Global Water Quality Assessment and Prediction with Explainable AI with LLM Chatbot for Insights
- 融合树模型与CNN-LSTM网络，捕捉水质数据时空特征。
- 水质指数预测平均RMSE为1.2，决定系数达0.99，分类准确率99%。
- 通过SHAP分析关键指标，配套智能聊天机器人辅助决策。
保障安全供水需有效监测水质，尤其在尼泊尔等发展中国家，污染风险较高。本文针对加拿大、中国、英国、美国和爱尔兰的CCME水质数据集（含282万条记录），采用多种混合深度学习模型进行特征工程与评估。使用CatBoost、XGBoost、Extra Trees及结合CNN与LSTM层的神经网络，捕捉数据中的时空模式。模型显著提升预测精度，助力主动水质管控。三种树模型对水质指数（WQI）的预测平均RMSE为1.2，决定系数达0.99；分类器在跨模型交叉验证中准确率达99%。SHAP分析表明F.R.C.和正磷酸盐水平在分类决策中起关键作用。研究还展示了实际应用，并开发了用于水质洞察的LLM聊天机器人。
原文摘要 · Abstract (English)
Ensuring safe water supplies requires effective water quality monitoring, especially in developing countries like Nepal, where contamination risks are high. This paper introduces various hybrid deep learning models to predict on the CCME dataset with multiple water quality parameters from Canada, China, the UK, the USA, and Ireland, with 2.82 million data records feature-engineered and evaluated using them. Models such as CatBoost, XGBoost, and Extra Trees, along with neural networks combining CNN and LSTM layers, are used to capture temporal and spatial patterns in the data. The model demonstrated notable accuracy improvements, aiding proactive water quality control. CatBoost, XGBoost, and Extra Trees Regressor predicted Water Quality Index (WQI) values with an average RMSE of 1.2 and an R squared score of 0.99. Additionally, classifiers achieved 99% accuracy, cross-validated across models. SHAP analysis showed the importance of indicators like F.R.C. and orthophosphate levels in hybrid architectures' classification decisions. The practical application is demonstrated along with a chatbot application for water quality insights.
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