Hazard and risk assessment of earthquake-induced landslides—case study

Bojadjieva, Julijana; Sheshov, Vlatko; Bonnard, C.

doi:10.1007/s10346-017-0905-9

Cited by 23 publications

(7 citation statements)

References 24 publications

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“…The first of these methods, pseudo-static analysis, proposed that the earthquake force represents an additional permanent physical force to statically conservation equations [10]. Despite the fact that selecting a pseudo-static coefficient requires criterion and always yields conservative results, pseudo-static modelling is theoretically simple [11]. Following that, the stressdeformation assessment method was proposed as an extension of finite-element simulation that is capable of simulating slope dynamic deformation.…”

Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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Machine learning has advanced rapidly in the last decade, promising to significantly change and improve the function of big data analysis in a variety of fields. When compared to traditional methods, machine learning provides significant advantages in complex problem solving, computing performance, uncertainty propagation and handling, and decision support. In this paper, we present a novel end-to-end strategy for improving the overall accuracy of earthquake detection by simultaneously improving each step of the detection pipeline. In addition, we propose a Conv2D convolutional neural network (CNN) architecture for processing seismic waveforms collected across a geophysical system. The proposed Conv2D method for earthquake detection was compared to various machine-learning approaches and state-of-the-art methods. All of the methods used were trained and tested on real data collected in Kazakhstan over the last 97 years, from 1906 to 2022. The proposed model outperformed the other models with accuracy, precision, recall, and f-score scores of 63%, 82.4%, 62.7%, and 83%, respectively. Based on the results, it is possible to conclude that the proposed Conv2D model is useful for predicting realworld earthquakes in seismic zones.

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Section: Introductionmentioning

confidence: 99%

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

Turarbek¹,

Adetbekov²,

Bektemesov³

2023

IJACSA

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show abstract

“…Among those models, pseudostatic analysis was the first proposed, assuming that the earthquake force is a permanent body force added to static limit-equilibrium analysis [18,19]. Although pseudostatic analysis is conceptually simple, selecting a pseudostatic coefficient lacks criteria, and crude assumption always leads to conservative results [20]. Stress-deformation analysis, as an extension of finite-element modeling, was later developed and provided a valuable tool for modeling the static and dynamic deformation of slopes.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past few years, the accuracy of physically based models has been enhanced significantly with improvements in analyzing EQIL mechanisms. However, PBMs require a large number of parameter values, which limits their application in a large geographical area [20].…”

Section: Introductionmentioning

confidence: 99%

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

Cui

et al. 2021

Remote Sensing

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An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. 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 to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.

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“…The substantial downside of the majority of approaches is the uncertainty regarding the earthquake scenario. Moreover, an accurate geomechanical model is difficult to build as the slope material parameters show not insignificant spatial and seasonal variability [14]. Deterministic, statistical, and pseudoprobabilistic methods are not able to take into account uncertainties caused by incomplete information about seismic and landslide processes and provide conservative estimates of seismically-induced landslide hazard.…”

Section: Introductionmentioning

confidence: 99%

Hazard-Consistent Earthquake Scenario Selection for Seismic Slope Stability Assessment

2020

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Design ground shaking intensity, based on probabilistic seismic hazard analysis (PSHA) maps, is most commonly used as a triggering condition to analyze slope stability under seismic loading. Uncertainties that are associated with expected ground motion levels are often ignored. This study considers an improved, fully probabilistic approach for earthquake scenario selection. The given method suggests the determination of the occurrence probability of various ground motion levels and the probability of landsliding for these ground motion parameters, giving the total probability of slope failure under seismic loading in a certain time interval. The occurrence hazard deaggregation technique is proposed for the selection of the ground shaking level, as well as the magnitude and source-to-site distance of a design earthquake, as these factors most probably trigger slope failure within the time interval of interest. An example application of the approach is provided for a slope near the highway in the south of Sakhalin Island (Russia). The total probability of earthquake-induced slope failure in the next 50 years was computed to be in the order of 16%. The scenario peak ground acceleration value estimated from the disaggregated earthquake-induced landslide hazard is 0.15g, while the 475-year seismic hazard curve predicts 0.3g. The case study highlights the significant difference between ground shaking scenario levels in terms of the 475-year seismic hazard map and the considered fully probabilistic approach.

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Hazard and risk assessment of earthquake-induced landslides—case study

Cited by 23 publications

References 24 publications

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

2-D Deep Convolutional Neural Network for Predicting the Intensity of Seismic Events

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

Hazard-Consistent Earthquake Scenario Selection for Seismic Slope Stability Assessment

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