Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM.

Area of Science:

• Environmental Science
• Hydrology
• Data Science

Background:

• Publicly available hydrological and water quality data in China support environmental management and research.
• Current data access is limited to real-time tables, hindering deeper analysis.

Purpose of the Study:

• To design a system for predicting water quality and providing early warnings for pollution risks.
• To leverage open data for enhanced water environmental protection and scientific investigation.

Main Methods:

• Utilized crawler technology for collecting real-time hydrological and water quality data.
• Implemented a modified Long Short-Term Memory (LSTM) model for water quality prediction.
• Integrated Geographic Information Technology (GIT) for visualizing spatio-temporal variations.

Main Results:

• The system effectively predicts water quality and pollution risks for key monitoring sites.
• It visualizes the spatial and temporal dynamics of hydrology and water quality across China.
• Provides timely evaluations and early warnings for water pollution events.

Conclusions:

• The developed system enhances the utilization of open hydrological and water quality data.
• It offers a valuable tool for water quality research, management, and pollution risk assessment.
• The system supports proactive water environmental protection strategies through data-driven insights.