GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China

Geomorphology ◽  
2010 ◽  
Vol 115 (1-2) ◽  
pp. 23-31 ◽  
Author(s):  
Shi-Biao Bai ◽  
Jian Wang ◽  
Guo-Nian Lü ◽  
Ping-Gen Zhou ◽  
Sheng-Shan Hou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges ◽  
Three Gorges Area ◽  
The Three Gorges

GIS-Based and Data-Driven Bivariate Landslide-Susceptibility Mapping in the Three Gorges Area, China

Pedosphere ◽  
2009 ◽  
Vol 19 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Shi-Biao BAI ◽  
Jian WANG ◽  
Guo-Nian LÜ ◽  
Ping-Gen ZHOU ◽  
Sheng-Shan HOU ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Data Driven ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges ◽  
Three Gorges Area ◽  
The Three Gorges

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

2018 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
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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