Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling

Aktaş, Hakan; San, Bekir Taner

doi:10.1016/j.cageo.2019.104329

Cited by 26 publications

(11 citation statements)

References 36 publications

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“…Then the corresponding decision tree model was acquired by training random selection of m attributes from all M decision attributes. Finally, the final classification result of the test dataset samples was determined by voting [22,31,34,35,[38][39][40][41]47].…”

Section: Random Forest (Rf) Modelmentioning

confidence: 99%

“…Statistical analysis method is difficult to accurately deal with the multi-source, heterogeneous, dynamic and massive landslide disaster-related data accumulated by long-term landslide disaster investigation [9]. The ML method has strong learning ability and can identify the non-linear relationship between landslide disaster susceptibility and influence factors in the region [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, more and more ML approaches have been optimized and applied for landslide susceptibility assessment in different regions. Examples are: Bayesian network (BN) [26,27], Naïve Bayes (NB) [19,27], artificial neural networks (ANN) [11,20,21,28,29], SVM [13,15,[18][19][20][21][22][23][24]27,[30][31][32], Logistic Regression (LR) [11,12,15,20,23,27,33,34], decision tree (DT) [19,22,30,[35][36][37], RF [22,31,[33][34][35][38][39][40][41], SVM-LR [23], convolutional neural network (CNN)-SVM, CNN-RF and CNN-LR [42]. These have all been used to quantitatively predict and assess the susceptibility for landslide in different regions of the world.…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Kong

Tian

et al. 2021

Remote Sensing

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Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

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Section: Random Forest (Rf) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Kong

Tian

et al. 2021

Remote Sensing

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“…In recent years, more and more machine-learning (ML) algorithms have been optimized and applied for landslide susceptibility assessment in different regions. Examples are: Bayesian network (BN) (Song et al, 2012;Pham et al, 2016), Naï ve Bayes (NB) (Tien Bui et al, 2012;Pham et al 2015Pham et al , 2016, artificial neural networks (ANN) (Choi et al, 2012;Zare et al, 2013;Conforti et al, 2014;Pham et al 2015;Xu et al, 2015;Tien Bui et al, 2016;Aditian et al, 2018;zhou et al, 2018), Support Vector Machines (SVM) (Marjanović et al, 2011;Tien Bui et al, 2012;2016;Pourghasemi et al, 2013;Pradhan, 2013;San, 2014;Kavzoglu et al, 2014;Peng et al, 2014;Hong et al 2015;Pham et al, 2016;Kumar et al, 2017;Ada and San, 2018;zhou et al, 2018;Aktas and San, 2019;Wang et al, 2019;Zhang et al, 2019), Logistic Regression (LR) (Choi et al, 2012;Kavzoglu et al, 2014;Hong et al 2015;Trigila et al, 2015;Pham et al, 2016; Tien Bui et al, 2016;Lin et al, 2017;Sevgen et al, 2019;Wang et al, 2019), decision tree (DT) (Tien Tien Bui et al, 2012;Pradhan, 2013;…”

Section: Introductionmentioning

confidence: 99%

“…CC BY 4.0 License. Khosravi et al, 2018;Aktas and San, 2019), Random Forest (RF) (Trigila et al, 2015;Youssef et al, 2016;Chen et al, 2017;Ada and San, 2018;Aktas and San, 2019), Fisher's linear discriminant analysis (FLDA) (Rossi et al, 2010; Murillo-Garcí a and Alcá ntara-Ayala, 2015), SVM-ANN (Xia et al, 2018), SVM-LR (Wang et al, 2019), convolutional neural network (CNN)-SVM, CNN-RF and CNN-LR (Fang et al, 2020). These have all been used to quantitatively predict and assess the susceptibility for landslide in different regions of the world.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment based on different machine-learning methods in Zhaoping County of eastern Guangxi

Kong

Wang

et al. 2020

Preprint

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Abstract. Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study were implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility by Zhaoping County. To this end, 10 landslide disaster-related causal variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were selected for running four machine-learning (ML) methods, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, and field investigation were performed to verify the efficiency of these models. Analysis and comparison of the results denoted that all four ML models performed well for the landslide susceptibility evaluation as indicated by the values of ROC curves – from 0.863 to 0.934. Moreover, the results also indicated that the PSO algorithm has a good effect on SVM and FR models. In addition, such a result also revealed that the PSO-RF and PSO-SVM models have the strong robustness and stable performance, and those two models are promising methods that could be transferred to other regions for landslide susceptibility evaluation.

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A frequency ratio–based sampling strategy for landslide susceptibility assessment

Liu

Zhang

Xiao

et al. 2022

Bull Eng Geol Environ

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Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling

Cited by 26 publications

References 36 publications

Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Landslide susceptibility assessment based on different machine-learning methods in Zhaoping County of eastern Guangxi

A frequency ratio–based sampling strategy for landslide susceptibility assessment

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