Occurrence probability assessment of earthquake-triggered landslides with Newmark displacement values and logistic regression: The Wenchuan earthquake, China

Geomorphology ◽  
2016 ◽  
Vol 258 ◽  
pp. 108-119 ◽  
Author(s):  
Ying Wang ◽  
Chongzhen Song ◽  
Qigen Lin ◽  
Juan Li
Keyword(s):  
Logistic Regression ◽  
Wenchuan Earthquake ◽  
Probability Assessment ◽  
Occurrence Probability ◽  
The Wenchuan Earthquake ◽  
Newmark Displacement

Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression

2013 ◽  
Vol 71 (2) ◽  
pp. 731-743 ◽  
Author(s):  
S. B. Bai ◽  
J. Wang ◽  
B. Thiebes ◽  
C. Cheng ◽  
Z. Y. Chang
Keyword(s):  
Logistic Regression ◽  
Wenchuan Earthquake ◽  
The Wenchuan Earthquake

Probability assessment of river blocking by debris flow associated with the Wenchuan Earthquake

2010 ◽  
Vol 31 (13) ◽  
pp. 3465-3478 ◽  
Author(s):  
Jian-Qi Zhuang ◽  
Peng Cui ◽  
Yong-Gang Ge ◽  
Yi-Ping He ◽  
Ying-Hui Liu ◽  
...  
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
Probability Assessment ◽  
The Wenchuan Earthquake

Catastrophic debris flows triggered by the 20 August 2019 rainfall, a decade since the Wenchuan earthquake, China

Landslides ◽  
2021 ◽  
Author(s):  
Fan Yang ◽  
Xuanmei Fan ◽  
Srikrishnan Siva Subramanian ◽  
Xiangyang Dou ◽  
Junlin Xiong ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Debris Flows ◽  
The Wenchuan Earthquake

scholarly journals Valuable Clues for DCNN-Based Landslide Detection from a Comparative Assessment in the Wenchuan Earthquake Area

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5191
Author(s):  
Chang Li ◽  
Bangjin Yi ◽  
Peng Gao ◽  
Hui Li ◽  
Jixing Sun ◽  
...  
Keyword(s):  
Wenchuan Earthquake ◽  
Remote Sensing Images ◽  
Machine Learning Methods ◽  
The Wenchuan Earthquake ◽  
Causative Factors ◽  
Multiple Structures ◽  
Sample Set ◽  
Larger Sample ◽  
Structure Layer ◽  
Earthquake Area

Landslide inventories could provide fundamental data for analyzing the causative factors and deformation mechanisms of landslide events. Considering that it is still hard to detect landslides automatically from remote sensing images, endeavors have been carried out to explore the potential of DCNNs on landslide detection, and obtained better performance than shallow machine learning methods. However, there is often confusion as to which structure, layer number, and sample size are better for a project. To fill this gap, this study conducted a comparative test on typical models for landside detection in the Wenchuan earthquake area, where about 200,000 secondary landslides were available. Multiple structures and layer numbers, including VGG16, VGG19, ResNet50, ResNet101, DenseNet120, DenseNet201, UNet−, UNet+, and ResUNet were investigated with different sample numbers (100, 1000, and 10,000). Results indicate that VGG models have the highest precision (about 0.9) but the lowest recall (below 0.76); ResNet models display the lowest precision (below 0.86) and a high recall (about 0.85); DenseNet models obtain moderate precision (below 0.88) and recall (about 0.8); while UNet+ also achieves moderate precision (0.8) and recall (0.84). Generally, a larger sample set can lead to better performance for VGG, ResNet, and DenseNet, and deeper layers could improve the detection results for ResNet and DenseNet. This study provides valuable clues for designing models’ type, layers, and sample set, based on tests with a large number of samples.

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