Literature DB >> 27810740

A spatial fuzzy logic approach to urban multi-hazard impact assessment in Concepción, Chile.

Dahyann Araya-Muñoz1, Marc J Metzger2, Neil Stuart2, A Meriwether W Wilson2, Danilo Carvajal3.   

Abstract

Even though most cities are exposed to more than one hazard, local planners and decision-makers still have a limited understanding of the exposure and sensitivity to and the spatial distribution of hazards. We examine the impact of multiple hazards in the Concepción Metropolitan Area (CMA), Chile. A flexible methodology based on spatial fuzzy logic modelling was developed to explore the impact of weather-related hazards, including coastal flooding, fluvial flooding, water scarcity, heat stress, and wildfire. 32 indicators were standardised and then aggregated through a stepwise approach into a multi-hazard impact index. We find that all the municipalities in the CMA increased their level of impact between 1992 and 2002, due to a larger increase in the exposure rather than the modest decrease in sensitivity. Municipal sensitivity was driven mostly by changes in the population's age structure. Wildfires and water scarcity appeared to have the largest impact on all municipalities. Fuzzy modelling offered high flexibility in the standardisation and aggregation of indicators with diverse characteristics, while also providing a means to explore how the interaction of numerous indicators influenced the index. The resulting maps can help identify indicators, components, and hazards or combinations of hazards that most influence the impact on municipalities. The results can be used to improve and promote dialogue among policy-makers and stakeholders regarding prioritisation of resources for urban development in ways that can also reduce exposure and sensitivity and lower vulnerability to climate change. The methods presented can be adapted to other cities.
Copyright © 2016 Elsevier B.V. All rights reserved.

Keywords:  Bottom-up evaluation; Developing countries; Fuzzy modelling; Geographical information system (GIS); Vulnerability

Year:  2016        PMID: 27810740     DOI: 10.1016/j.scitotenv.2016.10.077

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


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