Multi-criteria coastal environmental vulnerability assessment using analytic hierarchy process based uncertainty analysis integrated into GIS

Thirumurthy, S.; Jayanthi, M.; Samynathan, Muthusamy; Duraisamy, Muthusamy; Kabiraj, Sabyasachi; Anbazhahan, Nadesan

doi:10.1016/j.jenvman.2022.114941

Cited by 27 publications

(16 citation statements)

References 34 publications

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“…Since the PSR framework can effectively link environmental and human factors together, it is a useful indicator evaluation model, so the PSR model was used in this study. There were also evaluation calculation methods such as fuzzy mathematical method (Cheng et al, 2020;Li et al, 2020), principal component analysis (Salata and Grillenzoni, 2021;Yang 2021;Boori et al, 2021;Wei et al, 2020;Sun et al, 2020), artificial neural network method (Bordbar et al, 2021;Song et al, 2021;Tian et al, 2022), entropy power method (He et al, 2021;Sun,2021;Zhao et al, 2018;Wang, 2022;Yu et al, 2020), hierarchical analysis (Rehman et al, 2021;Liu et al, 2022;Min and Pan, 2022;Kumar et al,2021;Thirumurthy et al, 2022), landscape ecology method (Maiti et al, 2022;Yang et al, 2022;Tavakoli et al, 2022) data processing models, etc. From the results of the above studies, although there were various evaluation models, a single model had been used in most of the current studies, and no hybrid model had been utilized to evaluate the ecological vulnerability of the region.…”

Section: The Introductionmentioning

confidence: 99%

WITHDRAWN: A PCA-EM hybrid model based on the spatial pattern of ecological vulnerability and correlation study

Hou

2022

Preprint

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Ecological vulnerability reflects the ability of an ecosystem to resist adverse factors, and has become a global research hotspot. As an energy city in China and a city in the hinterland of the Loess Plateau, the study of Taiyuan's ecological vulnerability is of great importance. Most of the studies had used individual models, whose indicator weights were subject to large human factors or large compression of indicator information. In this study, an indicator evaluation system was developed based on the pressure state response (PSR). In order to overcome the shortcomings of a single model, a hybrid data processing model based on principal component analysis-entropy method (PCA-EM) was constructed in this study to study the ecological vulnerability of the area and compared with the principal component analysis model (PCA). Meanwhile, this study used Pearson correlation coefficients for analysis to evaluate the model performance and the influence of each parameter on ecological vulnerability. The research results illustrated that from 2000 to 2019, the EVI of Taiyuan showed a trend of first increasing and then gradually decreasing, indicating that it’s ecology was gradually improving. By comparing PCA method and PCA-EM method, the PCA-EM model could more objectively and reasonably evaluate the ecological environment of the region. Most of the indicators were negatively correlated with EVI, the highest correlation coefficient between per capita arable land and EVI. Furthermore, this study proposed a hybrid model to explore the ecological vulnerability status and influencing factors of the study area, providing an idea for data processing model.

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Section: The Introductionmentioning

confidence: 99%

WITHDRAWN: A PCA-EM hybrid model based on the spatial pattern of ecological vulnerability and correlation study

Hou

2022

Preprint

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“…Yu et al (2019) proved that there is a high spatial coupling between spaces with high levels of vitality and actual aggregation of activities using the open-sourced big data of streetscape images. Besides, there are also studies utilizing geographical information systems (GISs) to classify and identify coastal zones based on high-precision satellite maps (Balasubramanian et al, 2022;Thirumurthy et al, 2022).…”

Section: The Vitality Of Urban Coastal Zonesmentioning

confidence: 99%

Environmental drivers of the vital urban coastal zones: An explorative case study based on the data-driven multi-method approach

Yin

Shao²,

Huilin³

et al. 2022

Front. Ecol. Evol.

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Success in delivering dynamic urban coastal zones is considered essential, as it brings enormous opportunities to the social, economic, ecological, and cultural development of the cities in addition to benefitting the coastal zones. However, the environmental drivers contributing to urban coastal zone vitality remain uncertain due to unclarified spatial boundaries and the influences of diverse characteristics from surrounding contexts. This study aims at exploring environmental drivers that can vitalize urban coastal zones and can inform an effective way to instruct design procedures. It sets out from clarifying the spatial boundaries of urban coastal zones and emphasizing the mutual connections among its spatial components. A data-driven multi-method approach is used to analyze spatial forms, traffic organizations, land uses, landscape characteristics, and coastal functions of the eight typical coastal cases selected in different countries. Results suggested that six typical coastal zone types can be classified based on landscape characteristics and coastal functions, while the other vitality-related aspects, spatial forms, traffic organizations, and land uses are analyzed to imply design requirements for each type. It is found that requirements on vitality-related aspects of the six types evidently vary with the coastal functions, but there are similarities among the types with similar landscape characteristics.

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“…Likewise, the complex feedback and simultaneous effects of biophysical and socio-environmental factors on vulnerability of the system requires a multivariate analysis approach, in which the vulnerability index is estimated by the collective effect of all factors. This allows decisionmakers and land planners to look at the vulnerability of the system as a co-existence of all vulnerability factors associated with the various sub-systems in coastal regions 14 , 15 . These factors, with respect to their quantitative and qualitative characteristics, can be classified into different groups, namely physical characteristics, hydroclimate, environmental factors, and socio-economic perspective and the shoreline vulnerability (Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

“…Current CVI development approaches e.g., Refs. 2 , 15 , 21 , 32 , 33 mainly rely on analyzing biophysical hazard drivers, with the final CVI consisting of geographical and hydroclimate factors to determine the degree of coastal vulnerability. However, coastal vulnerability is also impacted by socio-economic and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

Tanim

Goharian

Moradkhani

2022

Sci Rep

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Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals’ biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.

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Multi-criteria coastal environmental vulnerability assessment using analytic hierarchy process based uncertainty analysis integrated into GIS

Cited by 27 publications

References 34 publications

WITHDRAWN: A PCA-EM hybrid model based on the spatial pattern of ecological vulnerability and correlation study

WITHDRAWN: A PCA-EM hybrid model based on the spatial pattern of ecological vulnerability and correlation study

Environmental drivers of the vital urban coastal zones: An explorative case study based on the data-driven multi-method approach

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

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