Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping

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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Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest

SN Applied Sciences ◽

10.1007/s42452-020-3060-1 ◽

2020 ◽

Vol 2 (7) ◽

Cited By ~ 1

Author(s):

Emrehan Kutlug Sahin

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

Predictive Capability ◽

Gradient Boosting Machine ◽

Tree Methods

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PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan

Remote Sensing ◽

10.3390/rs13204129 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4129

Author(s):

Muhammad Afaq Hussain ◽

Zhanlong Chen ◽

Run Wang ◽

Muhammad Shoaib

Keyword(s):

Landslide Susceptibility ◽

Regional Scale ◽

Susceptibility Mapping ◽

Data Availability ◽

Landslide Inventory ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

Persistent Scatterer Interferometry ◽

Extreme Gradient Boosting ◽

Deformation Velocity

Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.

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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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A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs13081464 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1464

Author(s):

Zhu Liang ◽

Changming Wang ◽

Zhijie Duan ◽

Hailiang Liu ◽

Xiaoyang Liu ◽

...

Keyword(s):

Unsupervised Learning ◽

Hybrid Model ◽

Landslide Susceptibility ◽

Learning Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

Susceptibility Map ◽

Continuous Variables ◽

Human Beings

Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

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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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