Quantitative and semi-quantitative methods in flood hazard/susceptibility mapping: a review

Flood mitigation and risk management is a very challenging task that requires accurate identification of flood hazard/susceptible regions for adequate planning and management. Accurate determination of locations that are prone to flood hazards needs the application and adaptation of techniques that...

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Bibliographic Details
Published in:Arabian journal of geosciences 2021-06, Vol.14 (11), Article 941
Main Authors: Mudashiru, Rofiat Bunmi, Sabtu, Nuridah, Abustan, Ismail
Format: Article
Language:English
Subjects:
Decision analysis
Decision making
Disaster management
Disaster risk
Earth and Environmental Science
Earth science
Earth Sciences
Emergency preparedness
Environmental risk
Flood control
Flood hazards
Flood management
Flood mapping
Floods
Hazard assessment
Hydrodynamics
Identification
Learning algorithms
Literature reviews
Machine learning
Mapping
Methods
Mitigation
Multiple criterion
Natural disasters
Quantitative analysis
Regions
Review Paper
Risk management
Risk reduction
Uncertainty
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Summary:Flood mitigation and risk management is a very challenging task that requires accurate identification of flood hazard/susceptible regions for adequate planning and management. Accurate determination of locations that are prone to flood hazards needs the application and adaptation of techniques that will provide flood hazard/susceptibility maps with minimal uncertainty. Previous literature reviews on flood hazard analysis have focused on the flood hazard mapping methods such as hydrodynamic, conceptual, and multi-criteria decision-making (MCDM). Thus, this current study thoroughly reviews studies that applied MCDM, statistical, and machine learning (ML) methods in the identification of flood hazard/susceptible regions. The paper presents information about these methods, their integration in flood studies, strengths, limitations, uncertainty, and recent developments. Conclusively, the paper provided observations and recommendations to enhance the existing information for relevant knowledge in future studies. This will assist stakeholders and key policymakers in making good decisions for flood analysis for a sustainable climate disaster risk reduction management.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-021-07263-4