Household Level Vulnerability Analysis—Index and Fuzzy Based Methods

Abstract

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.

Spatially Supported Disaster Management: Introduction to the Special Issue “GI for Disaster Management”

“…This special issue explores some of them, with challenging ideas facing different components of spatial patterns related to ecological processes and the published articles are selected and extended versions from the Gi4DM Conference in 2019 in Prague, Czech Republic. Articles [5][6][7] deal with sea-level rise. In the last decades, forest fires became a major disaster phenomenon and Luis Paduva [8] developed some innovative techniques such as UAV-borne observations and compared them with sentinel data.…”