Recent developments in machine learning applications in landslide susceptibility mapping

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine...

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Main Authors: Lun, N.K., Liew, M.S., Matori, A.N., Zawawi, N.A.W.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19902 /
Published: American Institute of Physics Inc. 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036638306&doi=10.1063%2f1.5012210&partnerID=40&md5=d5275228ac8aaf7843bb3bea467a5749
http://eprints.utp.edu.my/19902/
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spelling utp-eprints.199022018-04-22T13:16:00Z Recent developments in machine learning applications in landslide susceptibility mapping Lun, N.K. Liew, M.S. Matori, A.N. Zawawi, N.A.W.A. While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field. © 2017 Author(s). American Institute of Physics Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036638306&doi=10.1063%2f1.5012210&partnerID=40&md5=d5275228ac8aaf7843bb3bea467a5749 Lun, N.K. and Liew, M.S. and Matori, A.N. and Zawawi, N.A.W.A. (2017) Recent developments in machine learning applications in landslide susceptibility mapping. AIP Conference Proceedings, 1905 . http://eprints.utp.edu.my/19902/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field. © 2017 Author(s).
format Article
author Lun, N.K.
Liew, M.S.
Matori, A.N.
Zawawi, N.A.W.A.
spellingShingle Lun, N.K.
Liew, M.S.
Matori, A.N.
Zawawi, N.A.W.A.
Recent developments in machine learning applications in landslide susceptibility mapping
author_sort Lun, N.K.
title Recent developments in machine learning applications in landslide susceptibility mapping
title_short Recent developments in machine learning applications in landslide susceptibility mapping
title_full Recent developments in machine learning applications in landslide susceptibility mapping
title_fullStr Recent developments in machine learning applications in landslide susceptibility mapping
title_full_unstemmed Recent developments in machine learning applications in landslide susceptibility mapping
title_sort recent developments in machine learning applications in landslide susceptibility mapping
publisher American Institute of Physics Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036638306&doi=10.1063%2f1.5012210&partnerID=40&md5=d5275228ac8aaf7843bb3bea467a5749
http://eprints.utp.edu.my/19902/
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score 11.62408

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