Coastal Vulnerability Mapping Using Geospatial Technologies in Cuddalore-Pichavaram Coastal Tract, Tamil Nadu, India.

Sankari, T. Siva; Chandramouli, A.R.; Gokul, K.; Surya, S; Saravanavel, J.

doi:10.1016/j.aqpro.2015.02.055

Cited by 36 publications

(12 citation statements)

References 6 publications

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“…Unlike most previous coastal vulnerability case studies (Cui et al, 2015; Le Cozannet et al, 2013; McLaughlin and Cooper, 2010; Orencio and Fujii, 2013; Sankari et al, 2015), this study was thoroughly documented with the collaboration and consultation of stakeholders. Moreover, the criteria scores were identified based on the responses to questionnaires; unfortunately, some decision-makers were unfamiliar with multi-criteria questionnaires.…”

Section: Resultsmentioning

confidence: 99%

“…Contrary to most other empirical model Coastal Vulnerability Index (CVI) case studies (Balica et al, 2013; Cui et al, 2015; McLaughlin and Cooper, 2010; Orencio and Fujii, 2013; Rangel-Buitrago et al, 2015; Sankari et al, 2015), this study documents the process of consultation with decision-makers and describes the use of a FAHP method, which is more sophisticated. A few studies, such as Le Cozannet et al (2013) and Sankari et al (2015), have used the Analytic Hierarchy Process (AHP) to assess the level of coastal vulnerability risk, but the classic AHP has some weaknesses in the process of assigning unclear weights (Hajkowicz et al, 2000; Yang and Chen, 2004). Many researchers who have studied the FAHP have provided evidence that this method shows relatively more flexibility, an ability to determine appropriate areas and a more appropriate description of these kinds of decision-making processes compared to traditional AHP methods (Aruldoss et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Using Fuzzy Analytic Hierarchy Process multi-criteria and automatic computation to analyse coastal vulnerability

Tahri

Maanan

et al. 2017

Progress in Physical Geography: Earth and Environment

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International audienceThis paper presents an integrated method to assess the vulnerability of coastal risks by applying the Fuzzy Analytic Hierarchy Process (FAHP) and spatial analysis techniques with a geographic information system (GIS). The coast of Mohammedia, located in Morocco, was chosen as the study site to implement and validate the proposed framework by applying a GIS-FAHP-based methodology. Coastal risk vulnerability mapping reflects multi-parametric causative factors such as sea level rise, significan twave height, tidal range, shoreline evolution, elevation, geomorphology and distance to an urban area. The results show that the coastline of Mohammedia is characterised by low, moderate and high levels of vulnerability to coastal risk. The high vulnerability areas are situated in the east at Monica and Sablettes beaches. This technical approach helps decision-makers to find optimal strategies and to minimise coastal risks. In comparison with other assessment methods, this approach involves rapid data processing and provides an improved means of sustainable and multi-objective coastal management. Keywords Coastal risk vulnerability, Fuzzy Analytic Hierarchy Process, Digital Shoreline Analysis System, coastal hazard, coastal management

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Analytic Hierarchy Process multi-criteria and automatic computation to analyse coastal vulnerability

Tahri

Maanan

et al. 2017

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

show abstract

“…Dengan keunggulan tersebut, metode CVI relatif lebih popular dibandingkan dengan metode lain yang digunakan dalam penilaian kerentanan garis pantai di berbagai negara, seperti; Kanada (Shaw et al, 1998), Australia (Abuodha and Woodroffe, 2006), Spanyol (Ojeda-Zújar et al, 2008), Yunani (Alexandrakis et al, 2009), Turki (Özyurt andErgin, 2010), dan India (Kumar et al, 2010;Sankari et al, 2015), termasuk di Indonesia (Disaptono, 2008). Metode CVI ini juga memiliki kekurangan yaitu data numerik yang dihasilkan (ranking dan skor indeks) tidak serta-merta dapat disetarakan dengan dampak fisik tertentu.…”

Section: Pendahuluanunclassified

Penilaian Kerentanan Pantai Di Wilayah Pesisir Kabupaten Tuban Terhadap Ancaman Kerusakan

Joesidawati

2016

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Coastal Vulnerability analysis was conducted in the northern coast of East Parameter of coastal vulnerability index (CVI) that is used in weighting the beach Vulnerability to threats of physical damage using 10 variables, namely: (1). Coastal geomorphology (GF), (2) Elevation (E), (3) Stables tide (Tidal Range) Average (TR), (4) Significant Wave Height (SHW) (5) Sea Level Rise relative (SLR), (6) Changes in Coastline (PGP), (7) Land Use (PL), (8) lithology (L), (9) area of Damage Coast (KP), (10) The width of the green belt (SH

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“…One such method of need assessment is through the lens of 'vulnerability' analysis, which articulates the reality that hazards and subsequent assistance impact various population groups in grossly heterogeneous ways [11]. Numerous vulnerability indices have emerged in fields including infectious diseases [12,13], environmental health [14,15], disaster preparedness [16][17][18], refugee services [7,19], and climate change [20][21][22] which incorporate diverse, cross-sectoral indicators such as socio-economic status, education, information access, mobility, health and morbidity, security, and geographic location.…”

Section: Introductionmentioning

confidence: 99%

Gender-based vulnerability: combining Pareto ranking and spatial statistics to model gender-based vulnerability in Rohingya refugee settlements in Bangladesh

2020

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Background: The Rohingya refugee crisis in Bangladesh continues to outstrip humanitarian resources and undermine the health and security of over 900,000 people. Spatial, sector-specific information is required to better understand the needs of vulnerable populations, such as women and girls, and to target interventions with improved efficiency and effectiveness. This study aimed to create a gender-based vulnerability index and explore the geospatial and thematic variations in gender-based vulnerability of Rohingya refugees residing in Bangladesh by utilizing preexisting, open source data. Methods: Data sources included remotely-sensed REACH data on humanitarian infrastructure, United Nations Population Fund resource availability data, and the Needs and Population Monitoring Survey conducted by the International Organization for Migration in October 2017. Data gaps were addressed through probabilistic interpolation. A vulnerability index was designed through a process of literature review, variable selection and thematic grouping, normalization, and scorecard creation, and Pareto ranking was employed to rank sites based on vulnerability scoring. Spatial autocorrelation of vulnerability was analyzed with the Global and Anselin Local Moran's I applied to both combined vulnerability index rank and disaggregated thematic ranking. Results: Of the settlements, 24.1% were ranked as 'most vulnerable, ' with 30 highly vulnerable clusters identified predominantly in the northwest region of metropolitan Cox's Bazar. Five settlements in Dhokkin, Somitapara, and Pahartoli were categorized as less vulnerable outliers amongst highly vulnerable neighboring sites. Security-and health-related variables appear to be the most significant drivers of gender-specific vulnerability in Cox's Bazar. Clusters of low security and education vulnerability measures are shown near Kutupalong. Conclusion: The humanitarian sector produces tremendous amounts of data that can be analyzed with spatial statistics to improve research targeting and programmatic intervention. The critical utilization of these data and the validation of vulnerability indexes are required to improve the international response to the global refugee crisis. This study presents a novel methodology that can be utilized to not only spatially characterize gender-based vulnerability in refugee populations, but can also be calibrated to identify and serve other vulnerable populations during crises.

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Coastal Vulnerability Mapping Using Geospatial Technologies in Cuddalore-Pichavaram Coastal Tract, Tamil Nadu, India.

Cited by 36 publications

References 6 publications

Using Fuzzy Analytic Hierarchy Process multi-criteria and automatic computation to analyse coastal vulnerability

Using Fuzzy Analytic Hierarchy Process multi-criteria and automatic computation to analyse coastal vulnerability

Penilaian Kerentanan Pantai Di Wilayah Pesisir Kabupaten Tuban Terhadap Ancaman Kerusakan

Gender-based vulnerability: combining Pareto ranking and spatial statistics to model gender-based vulnerability in Rohingya refugee settlements in Bangladesh

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