水科学进展2026，Vol.37Issue(5)：880-892,13.DOI:10.14042/j.cnki.32.1309.2026.05.008
融合深度学习和大语言模型的可解释河流水质预测模型
An interpretable river water quality prediction model by integrating deep learning and large language models
摘要
Abstract
Physical mechanism-based models for river water quality prediction involve complicated calculations,whereas machine learning models lack interpretability,resulting in a disconnection between predictions and management decisions that hinders practical application.To deeply integrate high-precision prediction with decision support,a TCN-Attention deep learning model that combines a temporal convolutional network and an attention mechanism is constructed to predict six core water quality indicators,with Bayesian optimization used for automatic parameter tuning.The SHAP interpretability technique is introduced to quantify the contributions of multi-source input features and reveal key driving factors.Based on the Qwen3-Next large language model,water quality grade evaluations and improvement recommendations are automatically generated.Application results for rivers in Xinwu District of Wuxi City demonstrate that the TCN-Attention model achieves good prediction performance under small-sample conditions,with domestic water use,turbidity,and water identified as the most critical influencing factors.Qwen3-Next model achieves an accuracy of 89.8%in water quality grading,and the proposed improvement recommendations are targeted and practicable.The proposed interpretable intelligent water quality prediction method effectively improves the accuracy and interpretability of urban river water quality predictions,providing a reliable technical pathway for smart water environment management.关键词
河流水质预测/深度学习/大语言模型/可解释性分析/水环境管理Key words
river water quality prediction/deep learning/large language models/interpretable analysis/water environment management分类
建筑与水利引用本文复制引用
司徒祖祥,唐洪武,冉启华,程香菊..融合深度学习和大语言模型的可解释河流水质预测模型[J].水科学进展,2026,37(5):880-892,13.基金项目
国家自然科学基金项目(52539005 （）
52479064) The study is financially supported by the National Natural Science Foundation of China(No.52539005 （）
No.52479064). （）
