基于AI大数据模型的城市水环境质量在线监测与评估系统构建

Construction of an Online Monitoring and Evaluation System for Urban Water Environment Quality Based on AI Big Data Models

摘要: 随着城市化进程加快，水体污染问题日益严重，亟需更高效、智能的水环境监测与评估手段。本文围绕城市水质管理需求，构建了基于人工智能与大数据的在线监测与评估系统，系统涵盖数据采集、边缘处理、AI预测分析和可视化展示四大模块，采用随机森林和LSTM模型对多源水质数据进行污染等级分类与趋势预测。研究结合广州、深圳等地典型河段实地部署，验证系统在不同水体条件下的适用性与预测性能。结果表明，该系统在短时序预测中的准确率更高，响应效率明显优于传统手段。研究为城市水环境智能治理提供了技术路径，具备良好的现实推广价值，并为智慧环保系统建设提供理论支持。

Abstract: With the acceleration of urbanization, water pollution has become increasingly severe, necessitating more efficient and intelligent methods for water environment monitoring and evaluation. Addressing the needs of urban water quality management, this study constructs an online monitoring and evaluation system based on artificial intelligence (AI) and big data. The system comprises four key modules: data acquisition, edge processing, AI predictive analysis and visualization. It employs Random Forest and Long Short-Term Memory (LSTM) models to classify pollution levels and predict trends using multi-source water quality data. The research incorporates field deployments in typical river sections in Guangzhou, Shenzhen, and other cities, validating the system’s applicability and predictive performance under varying water conditions. The results demonstrate that the system achieves higher accuracy in short-term sequence forecasting and significantly outperforms traditional methods in response efficiency. This study provides a technical pathway for intelligent urban water environment governance, offering practical application value and theoretical support for the development of smart environmental protection systems.