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Lookup NU author(s): Top Phengsuwan, Dr Tejal Shah, Phillip James, Professor Stuart Barr, Professor Raj Ranjan
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.
Author(s): Phengsuwan J, Shah T, James P, Thakker D, Barr S, Ranjan R
Publication type: Article
Publication status: Published
Journal: Computing
Year: 2020
Volume: 102
Pages: 745-763
Online publication date: 13/06/2019
Acceptance date: 19/05/2019
Date deposited: 20/06/2019
ISSN (print): 0010-485X
ISSN (electronic): 1436-5057
Publisher: Springer Vienna
URL: https://doi.org/10.1007/s00607-019-00730-7
DOI: 10.1007/s00607-019-00730-7
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