Mapping disaster vulnerability in India using analytical hierarchy process

Chakraborty, Anusheema; Joshi, P. K.

doi:10.1080/19475705.2014.897656

Cited by 125 publications

(52 citation statements)

References 51 publications

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“…− Give the same importance to each factor; − Give an importance to each factor with the help of expert judgments and previous research works and then using analytical hierarchy process and correlation [35].…”

Section: Computation Of Integrated Social Vulnerability Index (Soviint)mentioning

confidence: 99%

Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India

Kumar¹,

Bhattacharjya

2020

Environmental and Climate Technologies

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AbstractThe hilly regions of India have suffered many disasters, both natural and anthropogenic. In the hilly state like Uttarakhand, the hazards like flash flood, forest fires, and landslide affect the community at the large scale. These hazards cause severe physical injuries, loss of life, and at large scale property damage. To understand the impact of such natural hazards, we need to examine vulnerability of the society, so that we can define vulnerability as the status of a community to prevent, mitigate, prepare for or respond to a natural and a man-made hazard. The absence of coping strategies, which is also known as resilience, has altered the vulnerability of a community. Thus, vulnerability index of a community has to be calculated considering physical, social, economic and environmental factors associated with the community. This research paper tries to find out an integrated social vulnerability factor. The proposed integrated social vulnerability factor is determined by considering various factors, such as physical, social, economic, and environmental. All these factors increase the susceptibility of a community to the impact of hazards. Poverty, occupation, child population, literacy rate, disability, marginalization, and inequities in wealth distribution of a society or community will also change the social vulnerability. Proposed Integrated social vulnerability index for the hilly terrain of Uttarakhand incorporated local technical knowledge insight and skills, so that local people and local administration are able to identify problems and can offer a solution to resist future emergencies i.e. the proposed social vulnerability indicator will support state, local, and traditional disaster management officials to determine areas of the most sensitive populations and better mitigation operation can be performed in case of disaster.

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Section: Computation Of Integrated Social Vulnerability Index (Soviint)mentioning

confidence: 99%

Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India

Kumar¹,

Bhattacharjya

2020

Environmental and Climate Technologies

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“…The different levels of AHP according to Saaty, 1980;Chakraborty and Joshi, 2014;Pourghasemi et al 2014;Papaioannou et al 2015;Nejad et al 2015) are: level 0: main objective, in present case flood risk map; level 1: Criteria analysis which are hazard map and vulnerability map, and level 3: element considered in each criteria characteristic based on their influence ( Figs. 6 and 7).…”

Section: Hierarchic Elaborationmentioning

confidence: 99%

“…But the use of other standardization approach such as linear instead of natural break (Jenks) can be improved for map comparison and accuracy assessment purposes. This methodological approach was inspired by various previous work Saley et al 2005;Mbow et al, 2008;Cozannet et al, 2013;Orencio, and Fujii, 2013;Chakraborty and Joshi, 2014;Pourghasemi et al, 2014;Papaioannou et al, 2015;Nejad et al, 2015) and it is clear that the risk of flooding is linked to combined action of many different factors under two criteria: hazard and vulnerability. However, the results can be improved by the development of urban structure types (UST) through oriented based image analysis (OBIA) method using high-resolution images (Ikonos, RapidEye, QuickBird) to raise classification details on urban morphology with good accuracy.…”

Section: Flood Riskmentioning

confidence: 99%

“…Of late, considerable attention has been given to the use of AHP in natural hazard (earthquake and flood) assessment but more in flood management in various studies: (Savane et al 2003;Yahaya et al 2010;Cozannet et al 2013;Orencio & Fujii, 2013;Saley et al 2013;Chakraborty and Joshi, 2014;Pourghasemi et al 2014;Papaioannou et al 2015;Nejad et al 2015). It has been shown from these series of papers that AHP has the ability to assess and map flood risk with good accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire)

Danumah

Odai

Saley

et al. 2016

Geoenviron Disasters

224

114

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Background: Flood is one of the most destructive natural disasters of climate change effects in West Africa. Flood risk occurrence is a combination of natural and anthropogenic factors, which calls for a better understanding of its spatial extent. The aim of this paper is to identify, and map areas of flood risk in Abidjan district. Results: This work is based on the integration of multi-criteria data including slope, drainage density, type of soil, Isohyet, population density, land use and sewer system density within ArcGIS interface. The resulting AHP flood risk map shows that areas under high and very high flood risk covers 34 % of the study area. Conclusion: The Analytic Hierarchy Process (AHP) method used as a multi-criteria analysis allowed the integration of several elements under two criteria, hazards and vulnerability, for flood risk assessment and mapping. Results revealed that, Abidjan district is heavily exposed to the risk of flooding. Eight out of thirteen (8/13) municipalities within the district are at a high risk of flooding which calls for decision makers to effectively develop strategies for future flood occurrences within the Abidjan district (South of Côte d'Ivoire).

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“…For example, Picozzi et al [42] devised an early warning tool for earthquakes which provides alert messages within about 5-10 s for seismic hazard zones, while Alfieri et al [2] analyzed a European operational warning tool for water-related disasters. Another line of works are focused on developing decision analytical models that provide a better understanding of how to use required variables, and thus improve decisionmaking [40,15,12]. Within this group, Kou and Wu [26] proposed a multi-criteria-based decision model that could be employed for analyzing existing medical resources and providing their optimal allocation.…”

Section: Decision-making Process In Control Roomsmentioning

confidence: 99%

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

Horita

Albuquerque

Marchezini

2018

International Journal of Disaster Risk Reduction

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warwick.ac.uk/lib-publications A B S T R A C TThe tasks of disaster risk monitoring and early warning are an important means of improving the efficiency of disaster response and preparedness. However, although the current works in this area have sought to provide a more accurate and better technological infrastructure of systems to support these tasks, they have failed to examine key features that may affect the decision-making. In light of this, this paper aims to provide an understanding of the decision-making process in control rooms for disaster risk monitoring and early warning. This understanding is underpinned by a conceptual framework, which has been developed in this work and describes factors that influence the decision-making. For doing so, data were collected through a series of semi-structured interviews and participatory observations and later evaluated with members of the control room of the Brazilian Center for Monitoring and Early Warning of Natural Disasters (Cemaden). The study findings provided a solid basis for designing the conceptual framework of the essential factors required by the decision-makers. These factors are separated into two groups: 1) the "dimensions" of decision-making (i.e., the type of hazard, the phase of the disaster risk, the location, and area of expertise of the operators) and the "pillars" of decision-making (i.e., the tasks, their required information, useful data sources, and the decision rule). Finally, the contributions achieved in this study may help operators to understand and propose proactive measures that could improve their decision-making, overcome uncertainties, standardize the team's decision-making, and put less pressure on operators.

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Mapping disaster vulnerability in India using analytical hierarchy process

Cited by 125 publications

References 51 publications

Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India

Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India

Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire)

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

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Mapping disaster vulnerability in India using analytical hierarchy process

Cited by 125 publications

References 51 publications

Study of Integrated Social Vulnerability Index SoVIint of Hilly Region of Uttarakhand, India

Study of Integrated Social Vulnerability Index SoVIint of Hilly Region of Uttarakhand, India

Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire)

Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in Brazil

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Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India

Study of Integrated Social Vulnerability Index SoVI_int of Hilly Region of Uttarakhand, India