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

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.

Publication metadata

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


DOI: 10.1007/s00607-019-00730-7


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