The Robustness Gap: Real-World Challenges in AI-Based Flood Severity Assessment

Abstract

Flood events are becoming more frequent and severe with rapid urbanization and climate change. With rising risks, accurate real-time assessments are critical for disaster management and operational decision-making, such as emergency response and resource allocation. Implementing artificial intelligence (AI), particularly deep learning (DL), has advanced the automation of estimating the damage from floods using satellite and sensor data. However, despite strong results in controlled environments, these models often suffer significant performance degradation under real-world conditions. This article assesses the state of the art in using AI to measure the severity of floods, paying specific attention to the robustness of the models and their general applicability to various environmental conditions. More than fifty primary studies published from 2019 through 2024 were analysed within the systematic literature review framework. The review proposes a diagnostic framework centered on three interconnected domains of difficulty: the quality and diversity of data, the model structure and ‘interpretability, and the authenticity of the environment, which includes turbidity, meteorological interference, and intricate topographical challenges. Results show that predictive reliability decreases significantly under sensor noise and unseen environmental conditions, although benchmark performance is usually high. There is a lack of analytic tools to measure the degree of these shortcomings. This review focuses on the growing “robustness gap” in artificial intelligence literature on flooding. It presents a vision for developing precise, adaptable, and functionally resilient AI capable of reliable deployment in operational flood disaster management.