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    Ontology-based discovery of time-series data sources for landslide early warning system

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    Publication date
    2020
    Author
    Phengsuwan, J.
    Shah, T.
    James, P.
    Thakker, Dhaval
    Barr, S.
    Ranjan, R.
    Keyword
    Time series data
    IoT data
    Early warning system
    Landslide hazard
    Smart city
    High variety data
    Ontology
    Data sources discovery
    Rights
    © The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
    Peer-Reviewed
    Yes
    
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    Abstract
    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.
    URI
    http://hdl.handle.net/10454/17176
    Version
    Published version
    Citation
    Phengsuwan J, Shah T, James P et al (2020) Ontology-based discovery of time-series data sources for landslide early warning system. Computing. 102: 745-763.
    Link to publisher’s version
    https://doi.org/10.1007/s00607-019-00730-7
    Type
    Article
    Collections
    Engineering and Informatics Publications

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