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An Improved Flood Susceptibility Model for Assessing the Correlation of Flood Hazard and Property Prices using Geospatial Technology and Fuzzy-ANP

A. Balogun1*, S. Quan1, B. Pradhan2,3,4,5, U. Dano6, and S. Yekeen1

  1. Geospatial Analysis and Modelling (GAM) Research Group, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), 32610 Seri Iskandar, Perak, Malaysia.
  2. Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia
  3. Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
  4. Center of Excellence for Climate Change Research, King Abdulaziz University, P. O. Box 80234, Jeddah 21589, Saudi Arabia
  5. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
  6. Department of Urban & Regional Planning, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 32141, Saudi Arabia Arabia

*Corresponding author Tel.: +6053687298; Email address:; (A Balogun)


This study proposes an integrated Geographic Information System (GIS)Fuzzy Multi-Criteria Decision Making (F-MCDM)model to assess the impacts of flood on residential property prices. Triangular Fuzzy numbers was implemented to address limitationssuch as uncertainty, bias and ambiguity inherent in the conventional Analytic Network Process (ANP) flood model criteria ranking thereby improving the accuracy and reliability of the susceptibility model. The developed Fuzzy-ANP’s (F-ANP) pair-wise comparison technique was used to rankthe relative importance of nine flood conditioning criteria based on experts’ input. Utilizing GIS and re-mote sensing data and techniques on Kelantan, a perennially flooded state in Malaysia, FANP based criterion maps weregenerated and aggregated to produce flood susceptibility maps of the area, showing the flood vulnerability levels of different locations. A 10-year inventory of real estate prices from the National Property Information Centre (NAPIC), Malaysia was analyzed to investigate the trend in market prices of residential properties situated in the high flood probable zones highlighted by the spatial F-ANP model. Model validation results showed that 59.42% and 36.23% of past flood events fall within the very high and high susceptible locations ofthe susceptibility map respectively, confirming its high accuracy. A weak positive correlation also exists between the highly susceptible flood class and housing locations vs market prices. We conclude that the ensemble GIS-FANP flood susceptibility modelcan produce maps capable of conveying accurate risk information to a broad range of stakeholders thereby facilitating decision making. However, other factors such as supply and demand, construction cost, macro-economy and micro-economy tend to also exert some influence on real estate prices, together with location in hazard-prone areas.

Keywords: flood; fuzzy ANP ; real estate prices ; GIS; RS; susceptibility

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