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  • Pham, Quoc BaoInstitute of Applied Technology, Thu Dau Mot University, Binh Duong province, Viet Nam (författare)

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

  • Artikel/kapitelEngelska2021

Förlag, utgivningsår, omfång ...

  • 2021-07-07
  • Taylor & Francis,2021
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:ltu-86407
  • https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-86407URI
  • https://doi.org/10.1080/19475705.2021.1944330DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

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

Anmärkningar

  • Validerad;2021;Nivå 2;2021-07-16 (johcin);Funder: Slovak Research and Development Agency (no. AP VV-18-0185); KEGA agency (no. 015UKF-4/2019); Deanship of Scientific Research, King Khalid University (no. RGP 2/173/42)
  • Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naive Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

Ämnesord och genrebeteckningar

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

  • Achour, YacineDepartment of Civil Engineering, Bordj Bou Arreridj University, El Annasser, Algeria (författare)
  • Ali, Sk AjimFaculty of Science, Department of Geography, Aligarh Muslim University, Aligarh, India (författare)
  • Parvin, FarhanaFaculty of Science, Department of Geography, Aligarh Muslim University, Aligarh, India (författare)
  • Vojtek, MatejDepartment of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia (författare)
  • Vojtekova, JanaDepartment of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia (författare)
  • Al-Ansari, Nadhir,1947-Luleå tekniska universitet,Geoteknologi(Swepub:ltu)nadhir (författare)
  • Achu, A. L.Department of Remote Sensing and GIS, Kerala University of Fisheries and Ocean Studies, Kochi, India (författare)
  • Costache, RomulusDepartment of Civil Engineering, Transilvania University of Brasov, Brasov, Romania (författare)
  • Khedher, Khaled MohamedDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia;i Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul, Tunisia (författare)
  • Anh, Duong TranHo Chi Minh City University of Technology (HUTECH) 475A, Ho Chi Minh City, Vietnam (författare)
  • Institute of Applied Technology, Thu Dau Mot University, Binh Duong province, Viet NamDepartment of Civil Engineering, Bordj Bou Arreridj University, El Annasser, Algeria (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Geomatics, Natural Hazards and Risk: Taylor & Francis12:1, s. 1741-17771947-57051947-5713

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