Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China

2021 ◽  
Author(s):  
Xudong Hu ◽  
Cheng Huang ◽  
Hongbo Mei ◽  
Han Zhang
Keyword(s):  
Landslide Susceptibility ◽  
Three Gorges Reservoir ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Ensemble Model ◽  
Random Subspace ◽  
Three Gorges ◽  
Reservoir 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.

Displacement prediction of Shengjibao landslide based on an ensemble model in Three Gorges Reservoir Area, China

2020 ◽  
Author(s):  
HongWei Jiang ◽  
Kunlong Yin ◽  
Thomas Glade
Keyword(s):  
Three Gorges Reservoir ◽  
Controlling Factors ◽  
Three Gorges Reservoir Area ◽  
Ensemble Model ◽  
Three Gorges ◽  
Displacement Monitoring ◽  
Single Factor ◽  
Displacement Prediction ◽  
Reservoir Area ◽  
The Three Gorges

<p>The Three Gorges Reservoir area (TGRA) is one of the most landslide-prone areas in China. Landslide prediction is important for the mitigating of geohazards and it is also an essential component for developing landslide early warning systems. In the TGRA, the preparatory, triggering and controlling factors of landslides are very diverse. The local geological conditions and variations in the controlling factors result in pulsed movements of landslides, the so-called “step-like” deformation of landslides. Most of the existing predictive models are based on a single algorithm including static models and dynamic models. This study proposes an Ensemble model combined with a static model and a dynamic model which combined the advantages of the two models for landslide displacement prediction.</p><p>Based on displacement monitoring data of the Shengjibao landslide in the Three Gorges Reservoir area(TGRA), which is not a typical “step-like” landslide but with the “step-like” characteristic in its displacement-monitoring curve, long short-term memory neural networks (LSTM) model, support vector regression (SVR) model and an Ensemble model based on LSTM model and SVR model were proposed to predict its displacement. Moving average methods (MAM), were used to decompose the cumulative displacement into two parts: trend and periodic terms. The single-factor LSTM model and the single factor SVR model were proposed to predict the trend terms of displacement. Multi-factors LSTM model and multi-factors SVR model were proposed to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for the multi-factors LSTM model and the multi-factors SVR model predictions. Meanwhile, an Ensemble model combined with the LSTM model and the SVR model was also proposed to predict the decompositions of displacement.</p><p>The results show that the LSTM model and the SVR model display good performance, the Ensemble model outperforms the other models, and the prediction accuracy can be improved by considering advantages from different models.</p>

scholarly journals Effects of Landscape Positions and Landscape Types on Soil Properties and Chlorophyll Content of Citrus in a Sloping Orchard in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (8) ◽  
pp. 4288
Author(s):  
Siyue Sun ◽  
Guolin Zhang ◽  
Tieguang He ◽  
Shufang Song ◽  
Xingbiao Chu
Keyword(s):  
Soil Properties ◽  
Chlorophyll Content ◽  
Three Gorges Reservoir ◽  
Slope Position ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landscape Types

In recent years, soil degradation and decreasing orchard productivity in the sloping orchards of the Three Gorges Reservoir Area of China have received considerable attention both inside and outside the country. More studies pay attention to the effects of topography on soil property changes, but less research is conducted from the landscape. Therefore, understanding the effects of landscape positions and landscape types on soil properties and chlorophyll content of citrus in a sloping orchard is of great significance in this area. Our results showed that landscape positions and types had a significant effect on the soil properties and chlorophyll content of citrus. The lowest soil nutrient content was detected in the upper slope position and sloping land, while the highest exists at the footslope and terraces. The chlorophyll content of citrus in the middle and upper landscape position was significantly higher than the footslope. The redundancy analysis showed that the first two ordination axes together accounted for 81.32% of the total variation, which could be explained by the changes of soil total nitrogen, total phosphorus, total potassium, available nitrogen, available potassium, organic matter, pH, and chlorophyll content of the citrus. Overall, this study indicates the significant influence of landscape positions and types on soil properties and chlorophyll content of citrus. Further, this study provides a reference for the determination of targeted land management measures and orchard landscape design so that the soil quality and orchard yield can be improved, and finally, the sustainable development of agriculture and ecology can be realized.

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