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

Farnaz Fotovatikhah, Manuel Herrera, Shahaboddin Shamshirband, Kwok Wing Chau, Sina Faizollahzadeh Ardabili, Md Jalil Piran

Research output: Journal article publicationJournal articleAcademic researchpeer-review

202 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)411-437
Number of pages27
JournalEngineering Applications of Computational Fluid Mechanics
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Big data
  • Computational intelligence
  • Flood management system
  • Natural hazard

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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