GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Ali, Sk Ajim; Parvin, Farhana; Vojteková, Jana; Costache, Romulus; Linh, Nguyễn Thị Thùy; Pham, Quoc Bao; Vojtek, Matej; Gigović, Ljubomir; Ahmad, Ateeque; Ghorbani, Mohammad Ali

doi:10.1016/j.gsf.2020.09.004

Cited by 170 publications

(82 citation statements)

References 103 publications

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“…Indeed, there are hundreds of recent studies that applied single or ensemble machine learning models. Based on models applied, it is very difficult to find any novelty amongst various studies, but each study is novel in term of geographical location and study findings because different outcomes have been found by applying same models in different geographical location (Achour and Pourghasemi 2020;Cao et al 2020;Dang et al 2020;Tien Bui et al 2020;Ali et al 2021). The main goal of this study was to present a comparative analysis amongst fuzzy MCDM, bivariate statistics, multivariate statistics and machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…The FDEMATEL method can be briefly represented in the following main steps. A detailed description of this method can be found, for example, in the studies by Ali et al (2021) or Kanani-Sadat et al (2019).…”

Section: Fuzzy Decision-making Trial and Evaluation Laboratory (Fdematel)mentioning

confidence: 99%

“…The present study analyzed and compared six heterogeneous models for LSI. For each model, the training and testing datasets were constructed using the common sampling strategy (Ali et al, 2021;Fang et al, 2021). Note that the estimated weight of each landslide model is significant for final LSI.…”

Section: Landslide Susceptibility Mapping Via Xgboostmentioning

confidence: 99%

“…In particular, DEMATEL method considers the interplay and dependence of decision factors, evaluates the association between the factors, and determines the weight of each relationship. In addition, the ANP method organizes the decision factors into a network of clusters and nodes (Kanani-Sadat et al 2019;Ali et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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

Pham

Achour

Ali

et al. 2021

Geomatics, Natural Hazards and Risk

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106

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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), 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 ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Section: Fuzzy Decision-making Trial and Evaluation Laboratory (Fdematel)mentioning

confidence: 99%

Section: Landslide Susceptibility Mapping Via Xgboostmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pham

Achour

Ali

et al. 2021

Geomatics, Natural Hazards and Risk

Self Cite

106

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“…Gaussian Naive Bayes(GNB) classifier is a classification method influenced by Bayes theorem that assumes all the features are entirely self-supporting given the output class described as restricted independence hypothesis [74]. It is convenient to manipulate naive Bayes classifier because it creates without requiring any complicated parameter calculation systems.…”

Section: Gaussian Naïve Bayes Classifiermentioning

confidence: 99%

Landslide Vulnerability Assessment in Devikulam Taluk, Idukki District, Kerala Using Gis and Machine Learning Algorithms

Gururajan¹,

Jawaharlal²

2021

Preprint

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Landslide is a chronic problem that causes severe geographical hazard due to development activities and exploitation of the hilly region and it occurs due to heavy and prolongs rain flow in the mountainous area. Initially, a total of 726 locations were identified at devikulam taluk, Idukki district (India). These landslide potential points utilised to construct a spatial database. Then, the geo spatial database is then split randomly into 70% for training the models and 30% for the model validation. This work considers Seven landslide triggering factors for landslide susceptibility mapping. The susceptibility maps were verified using various evaluation metrics. The metrics are sensitivity, specificity, accuracy, precision, Recall, Matthews correlation efficient (MCE), Area Under the Curve (AUC), Kappa statistics, Mean Absolute Error (MAE), Mean Square Error (MSE).The proposed work with 5 advanced machine learning approaches assess the landslide vulnerability.It includes Logistic Regression (LR), K Nearest Neighbor (KNN), Decision tree classifier, Linear Discriminant Analysis (LDA) and Gaussian Naïve Bayes modelling and comparing their performance for the spatial forecast of landslide possibilities in the Devikulam taluk. In experimental results, Decision tree classifier performs the most reliable performance with an overall accuracy rate of 99.21%.

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Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Gardezi¹,

Usmani

Chen

et al. 2022

Geological Journal

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GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Cited by 170 publications

References 103 publications

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

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

Landslide Vulnerability Assessment in Devikulam Taluk, Idukki District, Kerala Using Gis and Machine Learning Algorithms

Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

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