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Open AccessArticle

Household Level Vulnerability Analysis—Index and Fuzzy Based Methods

Faculty of Civil Engineering, Architecture and Geodesy, University of Split, Matice hrvatske 15, 21000 Split, Croatia
ISPRS Int. J. Geo-Inf. 2020, 9(4), 263;
Received: 31 January 2020 / Revised: 8 April 2020 / Accepted: 17 April 2020 / Published: 19 April 2020
(This article belongs to the Special Issue GI for Disaster Management)
Coastal vulnerability assessment due to climate change impacts, particularly for sea level rise, has become an essential part of coastal management all over the world. For the planning and implementation of adaptation measures at the household level, large-scale analysis is necessary. The main aim of this research is to investigate and propose a simple and viable assessment method that includes three key geospatial parameters: elevation, distance to coastline, and building footprint area. Two methods are proposed—one based on the Index method and another on fuzzy logic. While the former method standardizes the quantitative parameters to unit-less vulnerability sub-indices using functions (avoiding crisp classification) and summarizes them, the latter method turns quantitative parameters into linguistic variables and further implements fuzzy logic. For comparison purposes, a third method is considered: the existing Index method using crisp values for vulnerability sub-indices. All three methods were implemented, and the results show significant differences in their vulnerability assessments. A discussion on the advantages and disadvantages led to the following conclusion: although the fuzzy logic method satisfies almost all the requirements, a less complex method based on functions can be applied and still yields significant improvement. View Full-Text
Keywords: climate change; fuzzy logic; GIS, household; Index method; sea level rise; vulnerability climate change; fuzzy logic; GIS, household; Index method; sea level rise; vulnerability
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Baučić, M. Household Level Vulnerability Analysis—Index and Fuzzy Based Methods. ISPRS Int. J. Geo-Inf. 2020, 9, 263.

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