Research on Water Conservancy Information Processing and Analysis Methods Based on Machine Learning

With the new challenges of water resources management and protection, the demand for water information processing and analysis is increasing. This paper presents a comprehensive machine learning-based approach for processing and analyzing complex water data to support more precise water resource management decisions. In this paper, the main problems existing in the current water conservancy information system are described, including large amount of data, diverse types and lack of efficient forecasting and decision support tools. To address these issues, the present paper introduces a series of machine learning algorithms along with their utilization in the realm of water conservancy information processing. These include using supervised learning methods to predict river flows, using unsupervised learning techniques to identify anomalies in water quality monitoring data, and using reinforcement learning algorithms to optimize reservoir scheduling strategies. The performance of different machine learning algorithms is looked at through several real-world case studies.