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

Pham, Quoc Bao (author)
Institute of Applied Technology, Thu Dau Mot University, Binh Duong province, Viet Nam
Achour, Yacine (author)
Department of Civil Engineering, Bordj Bou Arreridj University, El Annasser, Algeria
Ali, Sk Ajim (author)
Faculty of Science, Department of Geography, Aligarh Muslim University, Aligarh, India
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Parvin, Farhana (author)
Faculty of Science, Department of Geography, Aligarh Muslim University, Aligarh, India
Vojtek, Matej (author)
Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia
Vojtekova, Jana (author)
Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia
Al-Ansari, Nadhir, 1947- (author)
Luleå tekniska universitet,Geoteknologi
Achu, A. L. (author)
Department of Remote Sensing and GIS, Kerala University of Fisheries and Ocean Studies, Kochi, India
Costache, Romulus (author)
Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania
Khedher, Khaled Mohamed (author)
Department 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
Anh, Duong Tran (author)
Ho Chi Minh City University of Technology (HUTECH) 475A, Ho Chi Minh City, Vietnam
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 (creator_code:org_t)
2021-07-07
2021
English.
In: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 1741-1777
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Geotechnical Engineering (hsv//eng)

Keyword

Fuzzy DEMATEL-ANP
bivariate frequency ratio
multivariate logistic regression
machine learning
landslide susceptibility mapping
Soil Mechanics
Geoteknik

Publication and Content Type

ref (subject category)
art (subject category)

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