A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam

Pham, Binh Thai; Nguyen, Viet-Tien; Ngo, Van-Liem; Trinh, Phan Trong; Ngo, Huong Thanh Thi; Bui, Dieu Tien

doi:10.1007/978-3-319-68240-2_12

Cited by 18 publications

(11 citation statements)

References 33 publications

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“…Using a linear support vector machine (LSVM) with 10-fold cross-validation, we identified the distance to road as the most significant factor for landslides in the Sarkhoon watershed. Similar findings have been previously reported by Pham et al [61,91,101]. The results of the factor selection also indicated that all other factors are important for the modeling and prediction of landslides in the Sarkhoon watershed.…”

Section: Discussionsupporting

confidence: 89%

“…where n is the total number of samples in the landslide training dataset or validation dataset, X obsevation is the predicted probability value in the landslide training dataset or validation dataset and X estimatin is the actual probability value calculated from the landslide susceptibility model. The areas under the receiver operating characteristic curve (AUC) is a standard tool for evaluating and assessing the general performance of models [27,49,66,85,101,102]. We used AUC to check the performance of our landslide models.…”

Section: Statistical Index-based Evaluationmentioning

confidence: 99%

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Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

et al. 2019

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We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.

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Section: Discussionsupporting

confidence: 89%

Section: Statistical Index-based Evaluationmentioning

confidence: 99%

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

et al. 2019

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“…It is constructed by using pairs of two values which are true positive rate and false negative rate [ 64 , 65 ]. Each point on this curve might be related to a specific decision criterion for the prediction accuracy; thus, the ROC curve is very useful for validating the predictive accuracy of models [ 66 , 67 , 68 , 69 ]. To quantitatively validate the models, area under this curve (AUC) is often used.…”

Section: Methodsmentioning

confidence: 99%

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Shirzadi

Soliamani

Habibnejhad

et al. 2018

Sensors

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166

102

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The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

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“…Two values are used to build the ROC curve: sensitivity and 100-specificity [69][70][71][72][73][74]. Performance of the models is analyzed quantitatively using the area under the curve (AUC) [75][76][77][78][79][80]. An AUC value of 1 indicates the best classification, while 0.5 corresponds to non-accurate models [81][82][83][84][85].…”

Section: Validation Methodsmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

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106

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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A Novel Hybrid Model of Rotation Forest Based Functional Trees for Landslide Susceptibility Mapping: A Case Study at Kon Tum Province, Vietnam

Cited by 18 publications

References 33 publications

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

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