Learning pattern of hurricane damage levels using semantic web resources
by Quang-Khai Tran; Sa-kwang Song
International Journal of Computational Science and Engineering (IJCSE), Vol. 20, No. 4, 2019

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.

Online publication date: Fri, 10-Jan-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com