流域断面水质监测及预测服务

基于公开的水质自动监测与手工监测数据、雨量及水文、排污许可数据、排污自行监测数据等，结合自有的河网空间数据、地形数据、遥感土地分类等专题数据，建立“源-网-厂-口-断面”的水质污染网络拓扑关系模型，利用神经网络建模预测，实现对水质监测点的短临24小时、7天水质变化情况预测，含常规五参及其他重点监测项。为科研机构、生态环境、水利及河流湖库监管部门提供数据支撑及监测服务。

Based on publicly available water quality automatic monitoring and manual monitoring data, rainfall and hydrological data, sewage discharge permit data, self-monitored sewage discharge data, etc., combined with proprietary spatial river network data, topographic data, remote sensing-based land classification and other thematic data, this dataset establishes a water quality pollution network topological model based on the "source-network-plant-outlet-monitoring section" framework. Using neural network modeling for prediction, it realizes short-lead time (24-hour and 7-day) water quality change forecasts at water quality monitoring sites, covering the five routine water quality parameters and other key monitoring items. It provides data support and monitoring services for scientific research institutions, ecological environment authorities, water conservancy departments, and river, lake and reservoir supervision authorities.