Seismic landslides hazard zoning based on the modified Newmark model: a case study from the Lushan earthquake, China

Ke, Jin; Yao, Lingkan; Cheng, Qiangong; Xing, Aiguo

doi:10.1007/s11069-019-03754-6

Cited by 23 publications

(20 citation statements)

References 30 publications

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“…We expand upon such studies by exploring the application of LiDAR, a variety of predictor variables and RF machine learning over a large spatial extent, which is uncommon in the literature. Although this study focuses on probabilistic mapping using the RF traditional machine learning method, is should be noted that deep learning methods that rely on convolutional neural networks have been explored for slope failure mapping and predictive tasks in several recent studies [25,29,30,[56][57][58][59][60][61].…”

Section: Mapping Slope Failures and Susceptibilitymentioning

confidence: 99%

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Maxwell

Sharma²,

Kite

et al. 2020

Remote Sensing

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The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping.

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Section: Mapping Slope Failures and Susceptibilitymentioning

confidence: 99%

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Maxwell

Sharma²,

Kite

et al. 2020

Remote Sensing

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“…Based on the margin of safety factor, a rainfall threshold distribution map was then produced, which was used as an input for the Newmark model which has previously been successfully implemented to predict the locations of shallow, unstable slopes induced by earthquakes 43 . We tested the ability of three regression methods (by Jibson et al 21 , Chousianitis et al 42 , and Jin et al 44 ) to calculate the permanent displacement of landslides using the hazard data during the 2011 Sikkim earthquake. The model with a highest accuracy by ROC curve 45,46 was chosen to predict landslide permanent displacement and failure probability considering rainfall conditions and future extreme earthquake intensities.…”

Section: Methodology and Datamentioning

confidence: 99%

“…The regression method allows for the calculation of the permanent displacement of slopes in terms of seismic intensity, morphological figures and the physical and mechanical parameters of the materials involved. For the landslides induced by the Sikkim earthquake in Yadong, we referred to the most suitable regression models among the previous studies 19,21,42,44 .…”

Section: Methodology and Datamentioning

confidence: 99%

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Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China

Chen

Mei

Zeng

et al. 2020

Sci Rep

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Yadong County located in the southern Himalayan mountains in Tibet, China, is an import frontier county. It was affected by landslides after the 2011 Sikkim earthquake (Mw = 6.8) and the 2015 Gorkha earthquake (Mw = 7.8). Casualties and property damage were caused by shallow landslides during subsequent rainfall on the earthquake-destabilized slopes. Existing researches have generally examined rainfall- and earthquake-triggered landslides independently, whereas few studies have considered the combined effects of both. Furthermore, there is no previous study reported on landslide hazards in the study area, although the area is strategically applicable for trade as it is close to Bhutan and India. This study developed a new approach that coupled the Newmark method with the hydrological model based on geomorphological, geological, geotechnical, seismological and rainfall data. A rainfall threshold distribution map was generated, indicating that the southeast part of Yadong is prone to rainfall-induced landslides, especially when daily rainfall is higher than 45 mm/day. Permanent displacement predictions were used to identify landslide hazard zones. The regression model used to calculate these permanent displacement values was 71% accurate. Finally, landslide probability distribution maps were generated separately for dry and wet conditions with rainfall of varying intensities. Results can serve as a basis for local governments to manage seismic landslide risks during rainy seasons.

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“…This method has attracted considerable attention from researchers due to its simple mechanical principle. For example, the method has been applied to analyze the 1994 Mw6.7 Northridge earthquake [3], the 1997 Ml4.4 Umbria Marche earthquake [4], the 1999 Ml = 7.3 Chi-Chi earthquake [5], [6], the 2008 Mw8.0 Wenchuan earthquake [7], the 2013 Mw7.0 Lushan earthquake [8], [9], the 2015 Nepal earthquake [10], the 2016 Kumamoto earthquake [11], and the 2017 Jiuzhaigou earthquake [12]. However, the prediction of the Newmark model requires detailed and accurate geotechnical parameters and ground motion parameters to calculate the displacement of the slope, which is often difficult to obtain under current technical conditions [13].…”

mentioning

confidence: 99%

Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping

Chen

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rapid and effective evaluation of landslide susceptibility after earthquakes is critical for various applications, such as emergency rescue, land planning, and disaster prevention. Current research suffers from the lack of a complete landslide inventory and sample selection uncertainty issues. To solve these problems, this study presents a landslide susceptibility mapping model that integrates one-class support vector machine (OCSVM) and an incomplete landslide inventory, which was established with the aid of change detection from bi-temporal Landsat images. Wenchuan County is selected as the study area to test the performance of the proposed method. The proposed method is also compared with standard two-class SVM that selects a sample randomly. Experimental results show that OCSVM can achieve better performance than SVM when only an incomplete landslide inventory is available. The findings of this study can be applied to determine regional landslide susceptibility after earthquakes and provide an essential reference for emergency response.

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Seismic landslides hazard zoning based on the modified Newmark model: a case study from the Lushan earthquake, China

Cited by 23 publications

References 30 publications

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Failure probability assessment of landslides triggered by earthquakes and rainfall: a case study in Yadong County, Tibet, China

Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping

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