Flood vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques

Vignesh, K. S.; Anandakumar, I.; Ranjan, Rajeev; Borah, Debashree

doi:10.1007/s40808-020-00997-2

Cited by 47 publications

(17 citation statements)

References 26 publications

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“…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…”

Section: Vulnerability Assessment Methods and A Brief Discussion On P...mentioning

confidence: 99%

“…In a single model, different vulnerability elements can be tested together. Social vulnerability is specifically connected to the failure of any party or society to deal with the repercussions of any occurrence (Wahab and Muhamad Ludin, 2018) (Wang et al, 2019) (Vignesh et al, 2021). The poorer part of the population is more vulnerable to flooding and other natural disasters.…”

Section: Discussionmentioning

confidence: 99%

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A systematic review of the flood vulnerability using geographic information system

Chan

Abid

Sulaiman

et al. 2022

Heliyon

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Section: Vulnerability Assessment Methods and A Brief Discussion On P...mentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A systematic review of the flood vulnerability using geographic information system

Chan

Abid

Sulaiman

et al. 2022

Heliyon

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“…Drainage density can be defined as the length of river channels per unit area of the basin, and it represents flow accumulation pathways (Arora et al 2019). The drainage network map of the study area is generated from DEM data using the hydrology tools, and drainage density is calculated by the line density tool in GIS (Vignesh et al 2021).…”

Section: Drainage Densitymentioning

confidence: 99%

“…Floods give rise to different landforms like erosional and depositional landforms. The geomorphological data is obtained from the Bhukosh-Geological Survey of India (GSI) (https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx), and classified into (a) structural hills, (b) denudational hills, (c) alluvial plains, (d) pediplain, and (e) floodplain (Vignesh et al 2021).…”

Section: Geomorphologymentioning

confidence: 99%

A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level

Gupta

Dixit

2022

Geocarto International

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Floods are hydrological disasters that can alter the physical, socioeconomic, and environmental settings of a region. The objective of the present study is to develop an efficient and reliable methodology to prepare a flood risk map for Assam, the North-eastern region (NER) of India, by the integration of hazard and vulnerability components. Three indices, namely flood hazard index (FHI), flood vulnerability index (FVI), and flood risk index (FRI), are developed using multi-criteria decision analysis (MCDA) -Analytical hierarchy process (AHP) approach in GIS environment for the regional and administrative level of Assam. The selected hazard and vulnerability indicators define the topographical, geological, meteorological, drainage characteristics, land use land cover, and demographical features of Assam. The results show that more than 70% of the total area lies in the moderate to very high FHI class, 57.37% have moderate to high FVI, and more than 50% have moderate to very high FRI class.

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“…AHP [3,12,15,16,[20][21][22]27,29,32,35,36,43,47,48,63,64,75,82,[85][86][87]89,90,92,94,97,98,102,103,105,108,113,114,116,[118][119][120][121][122]124,128,133] [ 42,65,74,96,101,111,117,123] [30] [17,…”

Section: Preparedness Recovery Responsementioning

confidence: 99%

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

Abdullah

Zainol

Thian

et al. 2022

BDCC

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The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment in allowing a comprehensive understanding of the current situation, readiness, preparation, resources, and others for decision assessment and disaster events planning. This study aims to facilitate the criteria identification and selection from a macro domain perspective in improving flood management planning. The objectives of this study are (a) to explore and identify potential and possible criteria to be incorporated in the current flood management plan in the macro domain perspective; (b) to understand the type of flood measures and decision goals implemented to facilitate flood management planning decisions; and (c) to examine the possible structured mechanism for criteria selection based on the decision analysis technique. Based on a systematic literature review and thematic analysis using the PESTEL framework, the findings have identified and clustered domains and their criteria to be considered and applied in future flood management plans. The critical review on flood measures and decision goals would potentially equip stakeholders and policy makers for better decision making based on a disaster management plan. The decision analysis technique as a structured mechanism would significantly improve criteria identification and selection for comprehensive and collective decisions. The findings from this study could further improve Malaysia Adaptation Index (MAIN) criteria identification and selection, which could be the complementary and supporting reference in managing flood disaster management. A proposed framework from this study can be used as guidance in dealing with and optimising the criteria based on challenges and the current application of Big Data and criteria in managing disaster events.

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Flood vulnerability assessment using an integrated approach of multi-criteria decision-making model and geospatial techniques

Cited by 47 publications

References 26 publications

A systematic review of the flood vulnerability using geographic information system

A systematic review of the flood vulnerability using geographic information system

A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

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