Review of Machine Learning Methods for Reservoir Operation in Flood Control

Abstract

Reservoir operation during flood events is a critical component of water resources management, requiring the balancing of flood risk mitigation, water supply, and system safety under significant uncertainty. Traditional rule-based and optimization methods often struggle to adapt to dynamic hydrological conditions and forecast errors. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing reservoir operation by improving inflow forecasting, learning operational policies, and enabling real-time decision-making. This review synthesizes recent advances in ML-based reservoir operation, including deep learning, ensemble learning, and reinforcement learning approaches. Special attention is given to integrating ML with hydrological models and optimization frameworks, along with effectively addressing uncertainty in flood control operations. The review highlights that while ML significantly improves predictive accuracy and operational flexibility, challenges remain in model interpretability, generalization, and real-world implementation. Future research directions are identified, focusing on physics-informed ML, explainable artificial intelligence, and adaptive real-time systems. This study provides a comprehensive understanding of the current state-of-the-art and outlines pathways toward intelligent and resilient reservoir operation systems.