Title: Learning pattern of hurricane damage levels using semantic web resources

Authors: Quang-Khai Tran; Sa-kwang Song

Addresses: Department of Big Data Science, University of Science and Technology, South Korea; Decision Support Technology Research Lab, Disaster Management HPC Technology Research Center, Convergence Technology Research Division, Korea Institute of Science and Technology Information, (34141) 245 Daehak-ro, Yuseong-gu, Daejeon, South Korea ' Department of Big Data Science, University of Science and Technology, South Korea; Decision Support Technology Research Lab, Disaster Management HPC Technology Research Center, Convergence Technology Research Division, Korea Institute of Science and Technology Information, (34141) 245 Daehak-ro, Yuseong-gu, Daejeon, South Korea

Abstract: This paper proposes an approach for hurricane damage level prediction using semantic web resources and matrix completion algorithms. Based on the statistical unit node set framework, streaming data from five hurricanes and damage levels from 48 counties in the USA were collected from the SRBench dataset and other web resources, and then trans-coded into matrices. At a time t, the pattern of possible highest damage levels at 6 hours into the future was estimated using a multivariate regression procedure based on singular value decomposition. We also applied soft-impute algorithm and k-nearest neighbours concept to improve the statistical unit node set framework in this research domain. Results showed that the model could deal with inaccurate, inconsistent and incomplete streaming data that were highly sparse, to learn future damage patterns and perform forecasting in near real-time. It was able to estimate the damage levels in several scenarios even if two-thirds of the relevant weather information was unavailable. The contributions of this work will be able to promote the applicability of the semantic web in the context of climate change.

Keywords: hurricane damage; statistical unit node set; matrix completion; SRBench dataset; streaming data.

DOI: 10.1504/IJCSE.2019.104435

International Journal of Computational Science and Engineering, 2019 Vol.20 No.4, pp.492 - 500

Received: 17 Dec 2016
Accepted: 12 Aug 2017

Published online: 10 Jan 2020 *

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