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Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment

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  • Bakhtiar Feizizadeh
  • Stefan Kienberger

Abstract

This research analyses the application of spatially explicit sensitivity and uncertainty analysis for GIS (Geographic Information System) multicriteria decision analysis (MCDA) within a multi-dimensional vulnerability assessment regarding flooding in the Salzach river catchment in Austria. The research methodology is based on a spatially explicit sensitivity and uncertainty analysis of GIS-CDA for an assessment of the social, economic, and environmental dimensions of vulnerability. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimise the associated uncertainty within each dimension of the vulnerability assessment. The methodology proposed for achieving this objective is composed of four main steps. The first step is computing criteria weights using the analytic hierarchy process (AHP). In the second step, Monte Carlo simulation is applied to calculate the uncertainties associated with AHP weights. In the third step, the global sensitivity analysis (GSA) is employed in the form of a model-independent method of output variance decomposition, in which the variability of the different vulnerability assessments is apportioned to every criterion weight, generating one first-order (S) and one total effect (ST) sensitivity index map per criterion weight. Finally, in the fourth step, an ordered weighted averaging method is applied to model the final vulnerability maps. The results of this research demonstrate the robustness of spatially explicit GSA for minimising the uncertainty associated with GIS-MCDA models. Based on these results, we conclude that applying the variance-based GSA enables assessment of the importance of each input factor for the results of the GIS-MCDA method, both spatially and statistically, thus allowing us to introduce and recommend GIS-based GSA as a useful methodology for minimising the uncertainty of GIS-MCDA.

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  • Bakhtiar Feizizadeh & Stefan Kienberger, 2017. "Spatially explicit sensitivity and uncertainty analysis for multicriteria-based vulnerability assessment," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 60(11), pages 2013-2035, November.
    • Handle: RePEc:taf:jenpmg:v:60:y:2017:i:11:p:2013-2035
    DOI: 10.1080/09640568.2016.1269643
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    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
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    8. de Castro-Pardo, Mónica & Pérez-Rodríguez, Fernando & Martín-Martín, José María & Azevedo, João C., 2019. "Modelling stakeholders’ preferences to pinpoint conflicts in the planning of transboundary protected areas," Land Use Policy, Elsevier, vol. 89(C).
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    10. Parviz Mohamadzadeh & Samereh Pourmoradian & Bakhtiar Feizizadeh & Ayyoob Sharifi & Mathias Vogdrup-Schmidt, 2020. "A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    11. Danish Farooq & Sarbast Moslem & Rana Faisal Tufail & Omid Ghorbanzadeh & Szabolcs Duleba & Ahsen Maqsoom & Thomas Blaschke, 2020. "Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures," IJERPH, MDPI, vol. 17(6), pages 1-14, March.
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    13. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.

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