Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

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Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping

Frontiers in Earth Science ◽

10.3389/feart.2021.712240 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shibao Wang ◽

Jianqi Zhuang ◽

Jia Zheng ◽

Hongyu Fan ◽

Jiaxu Kong ◽

...

Keyword(s):

Random Forest ◽

Decision Tree ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

The Loess Plateau ◽

Tree Model ◽

Validation Data ◽

Extreme Gradient Boosting

Landslides are widely distributed worldwide and often result in tremendous casualties and economic losses, especially in the Loess Plateau of China. Taking Wuqi County in the hinterland of the Loess Plateau as the research area, using Bayesian hyperparameters to optimize random forest and extreme gradient boosting decision trees model for landslide susceptibility mapping, and the two optimized models are compared. In addition, 14 landslide influencing factors are selected, and 734 landslides are obtained according to field investigation and reports from literals. The landslides were randomly divided into training data (70%) and validation data (30%). The hyperparameters of the random forest and extreme gradient boosting decision tree models were optimized using a Bayesian algorithm, and then the optimal hyperparameters are selected for landslide susceptibility mapping. Both models were evaluated and compared using the receiver operating characteristic curve and confusion matrix. The results show that the AUC validation data of the Bayesian optimized random forest and extreme gradient boosting decision tree model are 0.88 and 0.86, respectively, which showed an improvement of 4 and 3%, indicating that the prediction performance of the two models has been improved. However, the random forest model has a higher predictive ability than the extreme gradient boosting decision tree model. Thus, hyperparameter optimization is of great significance in the improvement of the prediction accuracy of the model. Therefore, the optimized model can generate a high-quality landslide susceptibility map.

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Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping

Geocarto International ◽

10.1080/10106049.2019.1641560 ◽

2019 ◽

pp. 1-23 ◽

Cited By ~ 15

Author(s):

Emrehan Kutlug Sahin ◽

Ismail Colkesen

Keyword(s):

Performance Analysis ◽

Decision Tree ◽

Ensemble Learning ◽

Landslide Susceptibility ◽

Learning Algorithms ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping

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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping

Landslides ◽

10.1007/s10346-014-0466-0 ◽

2014 ◽

Vol 11 (6) ◽

pp. 1063-1078 ◽

Cited By ~ 75

Author(s):

Omar F. Althuwaynee ◽

Biswajeet Pradhan ◽

Hyuck-Jin Park ◽

Jung Hyun Lee

Keyword(s):

Logistic Regression ◽

Decision Tree ◽

Landslide Susceptibility ◽

Regression Models ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Multivariate Logistic Regression ◽

Logistic Regression Models ◽

Interaction Detection ◽

Chi Squared

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Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8010004 ◽

2018 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 13

Author(s):

Yingxu Song ◽

Ruiqing Niu ◽

Shiluo Xu ◽

Runqing Ye ◽

Ling Peng ◽

...

Keyword(s):

Decision Tree ◽

Landslide Susceptibility ◽

Three Gorges Reservoir ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Three Gorges Reservoir Area ◽

Gradient Boosting ◽

Three Gorges ◽

The Three Gorges ◽

Auc Value

The main goal of this study is to produce a landslide susceptibility map in the Wanzhou section of the Three Gorges reservoir area (China) with a weighted gradient boosting decision tree (weighted GBDT) model. According to the current research on landslide susceptibility mapping (LSM), the GBDT method is rarely used in LSM. Furthermore, previous studies have rarely considered the imbalance of landslide samples and simply regarded the LSM problem as a binary classification problem. In this paper, we considered LSM as an imbalanced learning problem and obtained a better predictive model using the weighted GBDT method. The innovations of the article mainly include the following two points: introducing the GBDT model into the evaluation of landslide susceptibility; using the weighted GBDT method to deal with the problem of landslide sample imbalance. The logistic regression (LR) model and gradient boosting decision tree (GBDT) model were also used in the study to compare with the weighted GBDT model. Five kinds of data from different data source were used in the study: geology, topography, hydrology, land cover, and triggered factors (rainfall, earthquake, land use, etc.). Twenty nine environmental parameters and 233 landslides were used as input data. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, and the recall value were used to estimate the quality of the weighted GBDT model, the GBDT model, and the LR model. The results showed that the GBDT model and the weighted GBDT model had a higher AUC value (0.977, 0.976) than the LR model (0.845); the weighted GBDT model had a little higher AUC value (0.977) than the GBDT model (0.976); and the weighted GBDT model had a higher recall value (0.823) than the GBDT model (0.426) and the LR model (0.004). The weighted GBDT method could be considered to have the best performance considering the AUC value and the recall value in landslide susceptibility mapping dealing with imbalanced landslide data.

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Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping

CATENA ◽

10.1016/j.catena.2019.104396 ◽

2020 ◽

Vol 187 ◽

pp. 104396 ◽

Cited By ~ 28

Author(s):

Yanli Wu ◽

Yutian Ke ◽

Zhuo Chen ◽

Shouyun Liang ◽

Hongliang Zhao ◽

...

Keyword(s):

Decision Tree ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Alternating Decision Tree

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Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China

Water ◽

10.3390/w12113066 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3066

Author(s):

Guangzhi Rong ◽

Si Alu ◽

Kaiwei Li ◽

Yulin Su ◽

Jiquan Zhang ◽

...

Keyword(s):

Random Forest ◽

Decision Tree ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Bayesian Optimization ◽

Landslide Susceptibility Mapping ◽

Economic Losses ◽

Gradient Boosting ◽

Conditioning Factors ◽

Before And After

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

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