Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

TLDR

Flooding generates hazardous debris that threatens health and blocks roads, prompting the use of flood management systems that employ conventional AI and computational intelligence for early detection and efficient post‑disaster response. The study surveys CI‑based methods in flood management systems and identifies promising approaches for debris forecasting and management. CI methods are classified into single and hybrid approaches, with the survey highlighting the most accurate and low‑error techniques for flood debris forecasting. Ensemble CI approaches demonstrate high efficiency in flood prediction.

Abstract

Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people’s health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.