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

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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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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Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping

Geocarto International ◽

10.1080/10106049.2020.1831623 ◽

2020 ◽

pp. 1-25

Author(s):

Emrehan Kutlug Sahin

Keyword(s):

Comparative Analysis ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Gradient Boosting ◽

Boosting Algorithms

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A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping

Geocarto International ◽

10.1080/10106049.2018.1516248 ◽

2018 ◽

Vol 35 (4) ◽

pp. 341-363 ◽

Cited By ~ 16

Author(s):

Emrehan Kutlug Sahin ◽

Ismail Colkesen ◽

Taskin Kavzoglu

Keyword(s):

Logistic Regression ◽

Random Forest ◽

Landslide Susceptibility ◽

Canonical Correlation ◽

Comparative Assessment ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Rotation Forest ◽

Regression Methods ◽

Forest Rotation

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Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping

CATENA ◽

10.1016/j.catena.2018.12.035 ◽

2019 ◽

Vol 176 ◽

pp. 45-64 ◽

Cited By ~ 32

Author(s):

Haoyuan Hong ◽

Yamin Miao ◽

Junzhi Liu ◽

A-Xing Zhu

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping

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Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124206 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4206 ◽

Cited By ~ 3

Author(s):

Yue Wang ◽

Deliang Sun ◽

Haijia Wen ◽

Hong Zhang ◽

Fengtai Zhang

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Coincidence Degree ◽

Susceptibility Mapping ◽

Machine Learning Techniques ◽

Landslide Susceptibility Mapping ◽

Training Dataset ◽

Entire Area ◽

Conditioning Factors

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.

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Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy)

Landslides ◽

10.1007/s10346-010-0207-y ◽

2010 ◽

Vol 7 (4) ◽

pp. 433-444 ◽

Cited By ~ 87

Author(s):

Federico Cervi ◽

Matteo Berti ◽

Lisa Borgatti ◽

Francesco Ronchetti ◽

Federica Manenti ◽

...

Keyword(s):

Landslide Susceptibility ◽

Northern Apennines ◽

Susceptibility Mapping ◽

Reggio Emilia ◽

Landslide Susceptibility Mapping ◽

Predictive Capability ◽

Deterministic Methods

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Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Forests ◽

10.3390/f11040421 ◽

2020 ◽

Vol 11 (4) ◽

pp. 421 ◽

Cited By ~ 20

Author(s):

Viet-Ha Nhu ◽

Ataollah Shirzadi ◽

Himan Shahabi ◽

Wei Chen ◽

John J Clague ◽

...

Keyword(s):

Random Forest ◽

Landslide Susceptibility ◽

Goodness Of Fit ◽

Information Gain ◽

Characteristic Curve ◽

Model Performance ◽

Shallow Landslide ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Conditioning Factors

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

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