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ISPRS Int. J. Geo-Inf. 2015, 4(4), 1774-1790; doi:10.3390/ijgi4041774

Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index

1
Center for Natural and Technological Hazards, University of Utah, 260 S. Central Campus Dr. Rm. 270, Salt Lake City, UT 84112-9155, USA
2
Department of Geography, University of Utah, 260 S. Central Campus Dr., Rm. 270, Salt Lake City, UT 84112-9155, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Christoph Aubrecht and Wolfgang Kainz
Received: 4 May 2015 / Revised: 9 September 2015 / Accepted: 11 September 2015 / Published: 24 September 2015
(This article belongs to the Special Issue Geoinformation for Disaster Risk Management)
View Full-Text   |   Download PDF [6386 KB, uploaded 24 September 2015]   |  

Abstract

The Social Vulnerability Index (SoVI) has served the hazards community well for more than a decade. Using Utah as a test case, a state with a population exposed to a variety of hazards, this study sought to build upon the SoVI approach by augmenting it with a non-linear Artificial Neural Network (ANN). A SoVI was created for the state of Utah at the census block group level using five-year data (2008–2012) from the American Community Survey. The SoVI provided a dataset from which to train a neural network. The ANN was then used to classify a subset of the state to determine if it could provide a comparable classification of vulnerability. The ANN produced a vulnerability classification that was approximately 26% consistent with the SoVI created using the traditional approach. The differences in classifications were assessed using radar plots of block group variable averages to explore how the variables were handled in each classification. The results of this study warrant further investigation of the capabilities of an ANN-enhanced SoVI. View Full-Text
Keywords: artificial neural networks; social vulnerability; social vulnerability index; environmental hazards artificial neural networks; social vulnerability; social vulnerability index; environmental hazards
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hile, R.; Cova, T.J. Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index. ISPRS Int. J. Geo-Inf. 2015, 4, 1774-1790.

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