Riverine flood vulnerability assessment and zoning using geospatial data and MCDA method in Aq'Qala. (November 2022)

• Record Type: Journal Article
• Authors: Ganji, Kamran; Gharechelou, Saeid; Ahmadi, Ahmad; Johnson, Brian Alan
• Is Part Of: International journal of disaster risk reduction. Volume 82(2022)
• Publication Date: 2022-11
• Subjects: Flood vulnerability rate (FVR) -- Aq'Qala -- AHP -- Fuzzy logic -- Satellite data -- Gorganrood river; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2022.103345
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: Aq'Qala county has been categorized in high flood-prone areas in Iran, therefore it faced many major flood events in last two decades (e.g. in 2001, 2002, 2007, 2012, 2017, and 2019). Forecasting of flood-prone areas would be essential for planning of flood management strategies. Hence the main aim of the current study is to produce and validate flood vulnerability rate (FVR) map relying on geospatial data. Vulnerability criteria were identified through flood expert's consultations, field surveys and taking into consideration a flood event of 2019. Subsequently, the criteria clustered into five main classes: topography, climatology, human factors, geology and morphology. All sub-criteria were analysed using an analytical hierarchy process (AHP) questionnaire and fuzzification by triangular fuzzy number (TFN-AHP) in order to weighting. The FVR maps indicated that 26.1% (AHP map) to 29.66% (TFN-AHP map) of Aq'Qala have been located in very-high vulnerability rate. The least difference between AHP and TFN-AHP were 12.99% and 17.41% in very-high and high vulnerability rates, respectively. Moreover the least difference between the FVR maps and remote sensing flood mapping were 6.55% (AHP) and 2.33% (TFN-AHP). Base on time series satellite images-derived there was high compatibility between TFN-AHP map and flooding map of 2019 flood event. Results demonstrated that application of remote sensing data and Multi-Criteria methods simultaneously can provide reliable outputs.