Artificial Intelligence: The Transition from Response to Resilience in the Water Resources Crisis Management Cycle

The increasing frequency and severity of catastrophic events have made water resource crisis management a global priority, in which technological innovations play a pivotal role. The integration of artificial intelligence has fundamentally reshaped traditional crisis management paradigms and charted new paths to increase response effectiveness and operational efficiency. Despite these transformative impacts, several significant challenges and unresolved issues remain. This study systematically analyzed 258 studies indexed in the Web of Science and Google Scholar databases, focusing on the intersection of AI and disaster management, particularly water-related crises. Using a content analysis approach, the application of various AI technologies was examined across four standard phases of crisis management: prevention and mitigation, preparedness, response, and recovery. Furthermore, this study identified and synthesized research gaps in the current landscape of AI-based water crisis management, specifically addressing pre- and post-crisis resilience enhancement, strengthening critical infrastructure, optimizing evacuation network design, and long-term and sustainable post-disaster reconstruction. The findings provide important insights for policymakers, researchers, and practitioners while highlighting the untapped potential of advanced AI applications to drive further progress and innovation in the field of water crisis management.