AI-Driven Urban Water Infrastructure Planning Using Simulation Models for Sustainable Water Resource Management

Abstract

With the growing speed of urbanization, ageing water distribution systems, increasing population, climate variability and inefficient allocation of water resources, urban water infrastructure planning is becoming more difficult, resulting in water loss, disruptions in water supply and unsustainable water management practices. This study suggests an AI-based urban water infrastructure planning framework that combines hydraulic simulation, machine learning, and multi-objective optimization to solve these challenges and achieve sustainable water resource management. This methodology integrates hydraulic network simulation (EPANET) with a demand prediction model based on Extreme Gradient Boosting (XGBoost), temporal water consumption forecasting model based on Long Short-Term Memory (LSTM) networks and infrastructure optimization and pressure management model based on a Genetic Algorithm (GA). Varying demand, leakage and climate conditions were used in the simulation experiments to assess the resilience of the system and its operational efficiency. The proposed framework is compared with the conventional hydraulic planning, Random Forest and standalone LSTM models, and the results show that the proposed framework has achieved the highest accuracy of 97.2% in predicting demand, 95.8% in detecting leakage, reduced water losses by 31.6%, improved water pressure stability by 27.9%, reduced water operation energy consumption by 24.3%, and decreased decision making time by 22.7%. The framework also enhances the reliability of the network by 18.9% and the utilization of the resources by 26.4%. The novelty of this research is that it combines the use of AI-based predictive analytics for proactive urban water management with infrastructure optimization through simulation. The suggested framework offers an intelligent decision support system that improves the sustainability, operational resilience and efficiency of long-term planning of future smart water distribution networks.