Study on Integrated Watershed Management Decision-making Based on Artificial Intelligence

Abstract

Integrated watershed management plays an important role in achieving sustainable utilization of watersheds. Still, there are problems in current management processes such as insufficient data collection, limited model expressiveness, and reliance on personal experience in decision-making. These have become bottlenecks in advancing refined and intelligent watershed management. To resolve this contradiction, this study constructs an intelligent watershed management system based on artificial intelligence technologies. The system achieves efficient, comprehensive intelligent monitoring of the watershed environment by deploying sensor networks and using mobile measuring devices. Meanwhile, knowledge-based technologies are utilized to represent, store, and manage multi-source heterogeneous data. On this basis, techniques such as deep learning are used to establish digital twin and predictive models of the watershed to achieve accurate representations of the operating mechanisms of complex systems. Finally, the system can perform multi-scenario comparative analysis to assist decision-makers in scientifically formulating management strategies. Case studies demonstrate that the constructed system can make up for the deficiencies of traditional management methods and significantly improve the scientific and intelligent levels of watershed management. This research provides a systematic framework and technical approach for constructing an intelligent watershed management system, with important theoretical value and practical significance.