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Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Yao Li, Peng Cui, Chengming Ye, José Marcato Junior, Zhengtao Zhang, Jian Guo, Jonathan Li
2021 Remote Sensing  
Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related  ...  We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13173436 fatcat:xazqcxkr5vasfkk4llywihw5j4

Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

Pengfei Zhang, Chong Xu, Siyuan Ma, Xiaoyi Shao, Yingying Tian, Boyu Wen
2020 Remote Sensing  
In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction  ...  The study area is about 671.87 km2, of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model.  ...  Acknowledgments: Thanks for PLANET Company for providing us with pre-and post-earthquake images.  ... 
doi:10.3390/rs12233992 fatcat:wpxmbrwzbncfdpksqeiacl2ym4

Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province

Yulin Su, Guangzhi Rong, Yining Ma, Junwen Chi, Xingpeng Liu, Jiquan Zhang, Tiantao Li
2022 Frontiers in Earth Science  
To the best of our knowledge, the existing models are not suitable for the study of earthquake–geological disaster chains.  ...  Secondly, the DNN model is trained to calculate the susceptibility of landslides and is discussed with the Support Vector Machine (SVM) model, Logistic Regression (LR) model, and Random Forest (RF) model  ...  All authors have read and agreed to the published version of the manuscript. ACKNOWLEDGMENTS The authors are thankful to the reviewers for their useful suggestions.  ... 
doi:10.3389/feart.2021.683903 fatcat:cage2i2qxbgjjb5cvqo6mnfa6m

Support vector machine modeling of earthquake-induced landslides susceptibility in central part of Sichuan province, China

Suhua Zhou, Ligang Fang
2015 Geoenvironmental Disasters  
The aim of this study is to carry out prediction of earthquake-induced landslides distribution in the area affected by the April 20 2013 Lushan earthquake based on GIS and the SVM model.  ...  This paper provide an example for selecting appropriate types of kernel functions for prediction mapping of seismic landslides using support vector machine modeling.  ...  Acknowledgement This research is supported by State Key Development Program of Basic Research of China (Grant 2011CB710601) The data used in this paper was provided by the Department of Geotechnical Engineering  ... 
doi:10.1186/s40677-014-0006-1 fatcat:arfiskxrezfobopcuwp2u5co7a

A new strategy to map landslides with a generalized convolutional neural network

Nikhil Prakash, Andrea Manconi, Simon Loew
2021 Scientific Reports  
The trained model is then used to predict landslides in the surrounding regions.  ...  Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data.  ...  Velio Coviello at the Free University of Bozen-Bolzano, Italy, for providing valuable information on the Puebla-Morelos earthquake of 2017.  ... 
doi:10.1038/s41598-021-89015-8 pmid:33958656 pmcid:PMC8102623 fatcat:oia6pw7ytjev3mmtfixko5zhrq

Loess Landslide Detection Using Object Detection Algorithms in Northwest China

Yuanzhen Ju, Qiang Xu, Shichao Jin, Weile Li, Yanjun Su, Xiujun Dong, Qinghua Guo
2022 Remote Sensing  
Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images.  ...  segmentation method of deep learning.  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2022, 14, 1182  ... 
doi:10.3390/rs14051182 fatcat:bfzmfoh5jfas5czw62htdvqz54

Oral presentation speakers of Fifth World Landslide Forum

2021 Landslides. Journal of the International Consortium on Landslides  
Delft university of technology Netherlands Predicting rainfall induced slope stability using Random Forest regression and synthetic data Online 21 Ivan Depina SINTEF Norway Machine learning  ...  way to learning rainfall-induced landslides and its prevention using analog modelling Recorded Landslides 18 & (2021) 2685 Table 1 (continued) 1 No Speaker Organization Country/region  ... 
doi:10.1007/s10346-021-01680-y pmid:34054394 pmcid:PMC8144692 fatcat:akqrpjvljrd2vftcqho2lb7pfq

Landslide Extraction Using Mask R-CNN with Background-Enhancement Method

Ruilin Yang, Feng Zhang, Junshi Xia, Chuyi Wu
2022 Remote Sensing  
The application of deep learning methods has brought improvements to the accuracy and automation of landslide extractions based on remote sensing images because deep learning techniques have independent  ...  The experiment using both background-enhanced samples and landslide-inducing information showed a satisfying result with an F1 score of 89.08%.  ...  considered that landslide-inducing factors can provide valid landslide-related information for deep learning models.  ... 
doi:10.3390/rs14092206 fatcat:rncrcyyq6zh3jjnhn5fagk77jm

Toward the next generation of research on earthquake-induced landslides: Current issues and future challenges

Janusz Wasowski, David K. Keefer, Chyi-Tyi Lee
2011 Engineering Geology  
are still being learned from historic and recent earthquakes.  ...  We also offer some recommendations for future research priorities, as a proposed starting point for the next generation of research on earthquake-induced slope failures.  ...  of Taiwan) and the fruitful discussions with all the participants of the international conference "The Next Generation of Research on Earthquake-Induced Landslides" held in September 2009 at National  ... 
doi:10.1016/j.enggeo.2011.06.001 fatcat:nwn6phh6inejbalzgusw73fmd4

Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Y. Li, G. Chen, C. Tang, G. Zhou, L. Zheng
2012 Natural Hazards and Earth System Sciences  
in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map.  ...  Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps.  ...  Chen) from Japan Society for the Promotion of Science and is supported by the Program for Basic Research of the Ministry of Science and Technology, China (2011FY110103).  ... 
doi:10.5194/nhess-12-2719-2012 fatcat:sy6oorhmpjeq5bc6msfe7qtcp4

Shallow and Deep-Seated Landslide Differentiation Using Support Vector Machines: A Case Study of the Chuetsu Area, Japan

Jie Dou, Uttam Paudel, Takashi Oguchi, Shoichiro Uchiyama, Yuichi S. Hayakawa
2015 Terrestrial, Atmospheric and Oceanic Sciences  
The trained model was then used to prepare a map showing probable future landslides differentiated into shallow and deep-seated landslides.  ...  Shallow and deep-seated landslide prediction is useful in utilizing emergency resources by prioritizing target areas while responding to sediment related disasters.  ...  Acknowledgements The authors would like to express their sincere gratitude to the NIED for allowing us to use the landslide data. We deeply thank Prof.  ... 
doi:10.3319/tao.2014.12.02.07(eosi) fatcat:e4ed66gdz5fljaimxvidndkj4q

Understanding Landslide Susceptibility in Northern Chilean Patagonia: A Basin-Scale Study Using Machine Learning and Field Data

Elizabet Lizama, Bastian Morales, Marcelo Somos-Valenzuela, Ningsheng Chen, Mei Liu
2022 Remote Sensing  
For statistical modeling, we used an inventory of landslides that occurred between 2008 and 2017 and a total of 17 predictive variables, which are geoenvironmental, climatological and environmental [d=  ...  For this, we used two approaches, (1) geospatial data in combination with machine learning methods using different training configurations and (2) a qualitative analysis of the landscape considering the  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14040907 fatcat:vsblbhz5arbczosryynengg6tm

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Rui Liu, Xin Yang, Chong Xu, Liangshuai Wei, Xiangqiang Zeng
2022 Remote Sensing  
Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic  ...  The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression  ...  Additionally, we only used the typical models and network architecture and did not combine engineering geology analysis methods into the model.  ... 
doi:10.3390/rs14020321 fatcat:en6ktpo5sbbjhazo5fttdb24ue

Matrix SegNet: A Practical Deep Learning Framework for Landslide Mapping from Images of Different Areas with Different Spatial Resolutions

Bo Yu, Fang Chen, Chong Xu, Lei Wang, Ning Wang
2021 Remote Sensing  
It is a good starting point to develop a practical, deep learning landslide detection framework for large scale application, using images from different areas, with different spatial resolutions.  ...  The experiments show that our model improves the performance largely in terms of recall, precision, F1-score, and IOU.  ...  We trained our model on 70% of the cropped patches generated in Lushan earthquake-induced landslide images and evaluated the model structure on the rest patches.  ... 
doi:10.3390/rs13163158 fatcat:pphizndmkvbc5dfvknysx7k2dy

Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Xin Yang, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, Yuting Wang
2021 Remote Sensing  
A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were  ...  The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the  ...  The authors also would like to thank Kun Chen from the Institute of Geophysics, China Earthquake Administration for sharing the PGA data.  ... 
doi:10.3390/rs13112166 fatcat:vxhukghotzhpnctlw2v3vfvuxm
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