Groundwater Level Changes in Taiwan Caused by The Wenchuan Earthquake on 12 May 2008

2012 ◽  
Vol 169 (11) ◽  
pp. 1947-1962 ◽  
Author(s):  
Tsai-Ping Lee ◽  
Yeeping Chia ◽  
Hsin-Ying Yang ◽  
Ching-Yi Liu ◽  
Yung-Chia Chiu
Keyword(s):  
Wenchuan Earthquake ◽  
Groundwater Level ◽  
The Wenchuan Earthquake ◽  
Groundwater Level Changes

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.

scholarly journals Predicting groundwater level changes using GRACE data

2013 ◽  
Vol 49 (9) ◽  
pp. 5900-5912 ◽  
Author(s):  
Alexander Y. Sun
Keyword(s):  
Groundwater Level ◽  
Grace Data ◽  
Groundwater Level Changes

Resilience of adult survivors five years after the Wenchuan earthquake: A qualitative study

2020 ◽  
Vol 51 ◽  
pp. 101897
Author(s):  
Cuiping Ni ◽  
Ying Niu ◽  
Vico Chung-Lim Chiang ◽  
Xiaolian Jiang
Keyword(s):  
Qualitative Study ◽  
Wenchuan Earthquake ◽  
Adult Survivors ◽  
The Wenchuan Earthquake
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