Physical Flood Vulnerability Assessment using Geospatial Indicator-Based Approach and Participatory Analytical Hierarchy Process: A Case Study in Kota Bharu, Malaysia

Abstract

The most devastating flood event in Kota Bharu was recorded in December 2014, which affected several properties worth millions of dollars and thousands of homes. Damage to physical properties, especially buildings, is identified as a significant contributor to flood disasters in Malaysia. Therefore, it is essential to address physical flood vulnerability by developing an integrated approach for modeling buildings’ flood vulnerability to decrease the flood consequences. This study aims at developing a flood vulnerability assessment approach using an indicator-based model (IBM) for individual buildings in Kota Bahru, Kelantan, Malaysia. An intensive literature review and expert opinions were used to determine suitable indicators that contribute to the physical flood vulnerability of buildings. The indicators were grouped into three components, i.e., flood hazard intensity (I), building characteristics (C), and effect of the surrounding environment (E). The indicators were further refined based on expert opinions and Relative Importance Index (RII) analysis. Based on their contribution to the Malaysia local building flood vulnerability, priority weight is assigned by the experts to each of the selected indicators using the participatory Analytical Hierarchy Process (AHP). A spatial database of buildings in Kota Bharu is developed through field surveys and manually digitizing building footprints from satellite imageries. The identified indicators and their weight are added to each building footprint. The Weighted Linear Combination (WLC) aggregation method combined the weight of indicators into a vulnerability index and maps. The results of a physical flood vulnerability were validated using building damage information obtained through interviews with the community that experienced previous flood in the study area. The result showed that about 98% of the study area’s buildings have either moderate or low vulnerability to flooding. The flood vulnerability map has an overall accuracy of 75.12% and 0.63 kappa statistics. In conclusion, the IBM approach has been used successfully to develop a physical flood vulnerability for buildings in Kota Bharu. The model contributes to support different structural and non-structural approaches in the flood mitigations process.

A systematic review of the flood vulnerability using geographic information system

“…The peak discharge was calculated using the SWAT model, and the peak discharge was included as one of the indications. The General Circulation Models (GCM) data were used to forecast future rainfall, while Landsat pictures were used to create land use and land (Singh and Pandey, 2021), (Hosseini et al, 2021) (Nazeer and Bork, 2021), (Hussain et al, 2021) (Pathak et al, 2020), (Hoque et al, 2019) (Mavhura et al, 2017), (Terti et al, 2015) (Eidsvig et al, 2014), (Zhang, 2009) Physical vulnerability Interdependency analysis, indicator methodology, decision-making trial method., Indicator based approach, morphometric parameters were derived from SRTM DEM data using (GIS), Weighted Sum Approach (WSA), Principal Component Analysis (PCA), and an Integrated Approach (IA), GIS-Based Multi-Criteria Approach, Geospatial Indicator-Based Approach and Participatory Analytical Hierarchy Process, Flood generating factors: slope, elevation, land use/land cover, drainage density, rainfall, and soil types were rated and collected to mark out flood vulnerability zones using (GIS), Regression and GIS conditioning factors include digital elevation model (DEM), Pearson's correlation, multicollinearity, and heteroscedasticity analyses (Singh and Pandey, 2021), (Hosseini et al, 2021), (Nazeer and Bork, 2021), (Hussain et al, 2021), (Vignesh et al, 2021), (Usman Kaoje et al, 2021), (Desalegn and Mulu, 2021), (Usman Kaoje et al, 2021), (Sami et al, 2020), (D'Ayala et al, 2020), (Chuang et al, 2020), (Yin et al, 2019), (Hoque et al, 2019), Sajjad, 2019), Hübl et al, 2016), (Al-Juaidi et al, 2018), (Hazarika et al, 2018), (Walliman et al, 2012) and, (Mehebub et al, 2015) Environmental…”

Sustainable thin shell analysis on structural foundation subjected to seismic loads

“…In this study, the method used is an analytical method by obtaining parameters based on previous research paper on a case study in East coast, Malaysia. According to the study, the water average velocity v is 6.9 m/s, the average ground elevation, G is 1.69 m, and the flood elevation is 9.8 m from ground [15]. Table 6 shows the calculation of the wave load applied to the model, as the effect of the flood catastrophe.…”

Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings

“…Spatial data provide information on geographical delineations, locations, and patterns of flood damage concerning geographic coordinates and building information. Geographic information systems (GIS) have played a significant role in the pre-and post-processing of spatial inputs and outputs [3]. Flood risk assessment, flood hazard mapping, and flood inundation modeling are a few instances of the spatial analysis attributes of GIS [94].…”

“…Similarly, in 2021, Asia experienced the highest flood damage among all continents in the world, as shown in Figure 1. Furthermore, the recent climate change is expected to increase flood hazards in many regions worldwide [3,4]. In most regions, flooding disasters are caused by heavy rainfall [4][5][6].…”