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Landslide Susceptib...
Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms
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- Shahabi, Himan (författare)
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran; Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainability and Environment (RISE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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- Ahmadi, Reza (författare)
- Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran
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- Alizadeh, Mohsen (författare)
- Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Malaysia
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- Hashim, Mazlan (författare)
- Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainability and Environment (RISE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia; Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- Shirzadi, Ataollah (författare)
- Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran
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- Wolf, Isabelle D. (författare)
- Australian Centre for Culture, Environment, Society and Space, School of Geography and Sustainable Communities, University of Wollongong, Wollongong, NSW 2522, Australia; Centre for Ecosystem Science, University of New South Wales, Sydney, NSW 2052, Australia
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- Ariffin, Effi Helmy (författare)
- Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Malaysia; Faculty of Science and Marine Environment, Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Malaysia
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(creator_code:org_t)
- MDPI, 2023
- 2023
- Engelska.
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 15:12
- Relaterad länk:
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https://doi.org/10.3...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
Nyckelord
- landslides
- machine learning
- random forest
- support vector machine
- decision tree
- Kamyaran–Sarvabad road
- Geoteknik
- Soil Mechanics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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