Intelligent Decision Optimization Method for Water Information System Based on Artificial Intelligence

The traditional water management method struggles with delayed information acquisition and low decision-making efficiency, making it difficult to meet complex water resource management needs.To address this, the paper proposes an AI-based optimization method for water information systems.It uses sensor networks and IoT to collect real-time data, including water quality, quantity, and pipe pressure.Machine learning models historical data to identify key factors and risks.An optimization algorithm simulates and selects the best scheduling schemes.A visualization platform presents analysis results, aiding managers in decision-making.Users rated the platform features between 7 and 9 in satisfaction.This method is general, scalable, and applicable to various water systems, offering a useful reference for other fields.