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  • Nhu, Viet-HaGeographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam (författare)

Shallow Landslide Susceptibility Mapping : A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

  • Artikel/kapitelEngelska2020

Förlag, utgivningsår, omfång ...

  • 2020-04-16
  • Switzerland :MDPI,2020
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:ltu-78551
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-78551URI
  • https://doi.org/10.3390/ijerph17082749DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • Validerad;2020;Nivå 2;2020-04-27 (johcin)
  • Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Shirzadi, AtaollahDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (författare)
  • Shahabi, HimanDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran (författare)
  • Singh, Sushant K.Virtusa Corporation, Irvington, USA (författare)
  • Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
  • Clague, John J.Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada (författare)
  • Jaafari, AbolfazlResearch Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran, Iran (författare)
  • Chen, WeiCollege of Geology & Environment, Xi’an University of Science and Technology, Xi’an, China. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, China (författare)
  • Miraki, ShaghayeghDepartment of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran, Iran (författare)
  • Dou, JieDepartment of Civil and Environmental Engineering, Nagaoka University of Technology, Kami-Tomioka, Nagaoka, Niigata, Japan (författare)
  • Luu, ChinhFaculty of Hydraulic Engineering, National University of Civil Engineering, Hanoi , Vietnam (författare)
  • Górski, KrzysztofFaculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom, Poland (författare)
  • Pham, Binh ThaiInstitute of Research and Development, Duy Tan University, Da Nang, Vietnam (författare)
  • Nguyen, Huu DuyFaculty of Geography, VNU University of Science, Ha Noi, Vietnam (författare)
  • Ahmad, Baharin BinFaculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia (författare)
  • Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:International Journal of Environmental Research and Public HealthSwitzerland : MDPI17:8, s. 1-301661-78271660-4601

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