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

Geomatics, Natural Hazards & Risk

Yacine Achour¹  Khaled Mohamed Khedher²  Romulus Costache³  Nadhir Al-Ansari⁴  Matej Vojtek⁵  Jana Vojteková⁵  A. L. Achu⁶  Sk Ajim Ali⁷  Farhana Parvin⁷  Duong Tran Anh⁸  Quoc Bao Pham⁹ 

[1] Department of Civil Engineering, Bordj Bou Arreridj University;Department of Civil Engineering, College of Engineering, King Khalid University;Department of Civil Engineering, Transilvania University of Brasov;Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology;Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra;Department of Remote Sensing and GIS, Kerala University of Fisheries and Ocean Studies;Faculty of Science, Department of Geography, Aligarh Muslim University;Ho Chi Minh City University of Technology (HUTECH) 475A;Institute of Applied Technology, Thu Dau Mot University;

关键词: fuzzy dematel-anp;    bivariate frequency ratio;    multivariate logistic regression;    machine learning;    landslide susceptibility mapping;   

DOI  :  10.1080/19475705.2021.1944330

来源: DOAJ

【 摘 要 】

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), Naïve 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.

【 授权许可】

Unknown