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Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China
Yonghong Zhang, Taotao Ge, Wei Tian,
Yuei An Liou
太空及遙測研究中心
太空科學與工程學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
123
引文 斯高帕斯（Scopus）
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Keyphrases
China
100%
Machine Learning Techniques
100%
Debris Flow
100%
Debris Flow Susceptibility Mapping
100%
Debris Flow Susceptibility
83%
XGBoost
50%
Trigger Factor
50%
1-dimensional Convolutional Neural Network (1D-CNN)
50%
Back-propagation Artificial Neural Network (BP-ANN)
33%
Decision Tree
33%
Random Forest
33%
Geographic Information System
16%
Neural Network Method
16%
Average Rainfall
16%
F1 Score
16%
Precision-recall
16%
Evaluation Method
16%
Comparison Experiment
16%
Remote Sensing Information
16%
Area under the Curve
16%
Mountain Areas
16%
XGBoost Model
16%
Susceptibility Map
16%
Tibet
16%
10-fold Cross Validation
16%
Score Accuracy
16%
Method Precision
16%
Tukey's HSD Test
16%
Profile Curvature
16%
Borderline-SMOTE
16%
Soil Factors
16%
Human Activity Factor
16%
Profile Elevation
16%
Extreme Gradient Boosting(XGBoost)
16%
Susceptibility Level
16%
Engineering
One Dimensional
100%
Machine Learning Technique
100%
Convolutional Neural Network
100%
Machine Learning Method
66%
Random Forest
66%
Main Purpose
33%
Earth and Planetary Sciences
China
100%
Debris Flow
100%
Machine Learning
100%
Vegetation
8%
Geographic Information System
8%
Tibet
8%
Remote Sensing
8%
Chemical Engineering
Learning System
100%
Neural Network
100%