Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning

Fereshteh Taromideh, Ramin Fazloula, Bahram Choubin, Alireza Emadi and Ronny Berndtsson
Additional contact information
Fereshteh Taromideh: Department of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181-68984, Iran
Ramin Fazloula: Department of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181-68984, Iran
Bahram Choubin: Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia 57169-63963, Iran
Alireza Emadi: Department of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 48181-68984, Iran
Ronny Berndtsson: Centre for Advanced Middle Eastern Studies and Division of Water Resources Engineering, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden

Sustainability, 2022, vol. 14, issue 8, 1-22

Abstract: Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers.

Keywords: decision making; hazard; machine learning; risk; urban flood; vulnerability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/8/4483/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/8/4483/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:8:p:4483-:d:790219

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().