A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)

Erener, Arzu; Mutlu, Alev; Düzgün, Şebnem

doi:10.1016/j.enggeo.2015.09.007

Cited by 158 publications

(64 citation statements)

References 61 publications

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“…LR is a statistical technique that allows the predictor to analyze several types of variables [39][40][41]. LR does not require the normality assumption, which is an advantage over linear and log-linear regression.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

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Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

Syifa

Lee

et al. 2019

Applied Sciences

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To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions.

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

“…LR does not require the normality assumption, which is an advantage over linear and log-linear regression. The inclusion of multiple parameters offers the user the ability to select the best predictors for use in the model [39]. The LR model is formulated as follows [42]:…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

Syifa

Lee

et al. 2019

Applied Sciences

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“…Uddin et al [11] and Asborno et al [12] developed methods to evaluate the locations of transportation facilities. Additionally, for disaster management, Pourghasemi et al [13], Feizizadeh et al [14], Günther et al [15], Ahmed [16], Dragićevića et al [17], Erener et al [18] and Nsengiyumva et al [19] developed methods for landslide susceptibility assessment.…”

Section: Related Workmentioning

confidence: 99%

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2014

JCEA

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Abstract:The repair of reinforced concrete (RC) beams with carbon fiber reinforced polymers (CFRP) is experimentally investigated in this study. The four T-section reinforced reactive powder concrete beams with many locations of circular opening were repaired by CFRP and investigated under pure torsion. The repairing beams were tested to obtain the effect of the amount of CFRP laminate on beams cracking and ultimate torque behavior, angle of twist and failure modes. The results obtained from the adopted repairing technique showed a significant effect of external high strength CFRP laminates on effectively restore of section solid of 85% of crack torsional strength effectively restored. In addition, the results show that effectively restoring of section opening by 82%-80% of crack torsional strength, and the adopted repairing technique gives a good strength to the beams.

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“…Performance of the proposed method is evaluated on a real life dataset regarding Savsat region of Turkey. Savsat is a landslide intensive area located in the eastern part of Turkey and has been subject to several studies [9][10][11][12]. Experimental results based on 10-fold cross validation showed that the proposed method achieves 83.07% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Mutlu¹,

Göz²,

Koksal³

et al. 2019

Sakarya University Journal of Science

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Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study, we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set showed that the proposed method achieves 83.07% classification accuracy and is superior over most commonly used techniques for landslide susceptibility assessment such as logistic regression, support vector machines and artificial neural network.

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A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)

Cited by 158 publications

References 61 publications

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

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Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

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