Research Outline and Abstract of Intelligent Control-Based Water Body Health Prediction and Management

Abstract

The fundamental point of the ecological balance in water bodies and the safety of drinking water is the health of water bodies. The traditional models for managing water quality are no longer effective in dealing with the environmental challenges that are now more complex due to their lagging and low efficiency. This paper tries to examine and implement the use of intelligent technologies in building an intelligent water body health management system that would encompass the prediction, early warning, and decision support. On the one hand, the system of a multi-dimensional water body health assessment that involved physical, chemical, and biological indicators was created, and the need to standardize the data was explained. Then three fundamental intelligent prediction algorithms, namely BP neural network (BPNN), fuzzy logic, and support vector machine (SVM), were thoroughly discussed, explaining their working mechanism and merits and demerits in water quality prediction. The above models were trained and validated through real monitoring data for a given reservoir using a case study. The findings revealed that the combined model indicated a high degree of benefit in the accuracy of prediction and stability. Based on this, a three-layer intelligent management system architecture including the perception layer, platform layer, and application layer was designed, achieving a management closed loop from data collection, intelligent prediction to decision support. The research results not only provide efficient and forward-looking technical means for water environment supervision but also lay a solid theoretical and practical foundation for achieving the grand goal of "smart water management".