scholarly journals Empirical prediction for travel distance of channelized rock avalanches in the Wenchuan earthquake area

2016 ◽  
Author(s):  
Weiwei Zhan ◽  
Xuanmei Fan ◽  
Runqiu Huang ◽  
Xiangjun Pei ◽  
Qiang Xu ◽  
...  

Abstract. Rock avalanches are extremely rapid, massive flow-like movements of fragmented rock. The travel path of the rock avalanches may be confined by channels in some cases, which were named as the channelized rock avalanches. Channelized rock avalanches are potentially dangerous due to their hardly predictable travel distance. In this study, we constructed a dataset with detailed characteristic parameters of 38 channelized rock avalanches triggered by the 2008 Wenchuan earthquake using the visual interpretation of remote sensing imagery, field investigation, and literature review. Based on this dataset, we assessed the influence of different factors on the runout distance and developed prediction models of the channelized rock avalanches using the multivariate regression method. The results suggested that the movement of channelized rock avalanche was dominated by the landslide volume, total relief, and channel gradient. The performance of both models was then tested with an independent validation dataset of 8 rock avalanches that induced by the 2008 Wenchuan, the Ms7.0 Lushan earthquake, and heavy rainfall in 2013, showing acceptable good prediction results. Therefore, the travel distance prediction models for channelized rock avalanches constructed in this study is applicable and reliable for predicting the run out of similar rock avalanches in other regions.

2017 ◽  
Vol 17 (6) ◽  
pp. 833-844 ◽  
Author(s):  
Weiwei Zhan ◽  
Xuanmei Fan ◽  
Runqiu Huang ◽  
Xiangjun Pei ◽  
Qiang Xu ◽  
...  

Abstract. Rock avalanches are extremely rapid, massive flow-like movements of fragmented rock. The travel path of the rock avalanches may be confined by channels in some cases, which are referred to as channelized rock avalanches. Channelized rock avalanches are potentially dangerous due to their difficult-to-predict travel distance. In this study, we constructed a dataset with detailed characteristic parameters of 38 channelized rock avalanches triggered by the 2008 Wenchuan earthquake using the visual interpretation of remote sensing imagery, field investigation and literature review. Based on this dataset, we assessed the influence of different factors on the runout distance and developed prediction models of the channelized rock avalanches using the multivariate regression method. The results suggested that the movement of channelized rock avalanche was dominated by the landslide volume, total relief and channel gradient. The performance of both models was then tested with an independent validation dataset of eight rock avalanches that were induced by the 2008 Wenchuan earthquake, the Ms 7.0 Lushan earthquake and heavy rainfall in 2013, showing acceptable good prediction results. Therefore, the travel-distance prediction models for channelized rock avalanches constructed in this study are applicable and reliable for predicting the runout of similar rock avalanches in other regions.


2011 ◽  
Vol 71-78 ◽  
pp. 1736-1740 ◽  
Author(s):  
Xiu Zhen Li ◽  
Ji Ming Kong ◽  
Sheng Wei Li

Volume and slope are two important factors affecting the runout distance of landslides. Field investigation on 46 landslides triggered by the Wenchuan earthquake show that there are positive linear correlations between the logarithmic values of landslide volume and travel distance. And there is also a positive linear relationship between the equivalent friction coefficient and tangent value of initial slope angle for the landslides. On the basis, we obtained an empirical-statistic equation between the horizontal and vertical travel distance, the volume and initial slope angle. This can provide a basis for prediction of earthquake-induced landslides.


2021 ◽  
Author(s):  
Janusz Wasowski ◽  
Maurice McSaveney ◽  
Luca Pisanu ◽  
Vincenzo Del Gaudio ◽  
Yan Li ◽  
...  

<p>Large earthquake-triggered landslides, in particular rock avalanches, can have catastrophic consequences. However, the recognition of slopes prone to such failures remains difficult, because slope-specific seismic response depends on many factors including local topography, landforms, structure and internal geology. We address these issues by exploring the case of a rock avalanche of >3 million m<sup>3</sup> triggered by the 2008 Mw7.9 Wenchuan earthquake in the Longmen Shan range, China. The failure, denominated Yangjia gully rock avalanche, occurred in Beichuan County (Sichuan Province), one of the areas that suffered the highest shaking intensity and death toll caused by co-seismic landsliding. Even though the Wenchuan earthquake produced tens of large (volume >1 million m<sup>3</sup>) rock avalanches, few studies so far have examined the pre-2008 history of the failed slope or reported on the stratigraphic record of mass-movement deposits exposed along local river courses. The presented case of the Yangjia gully rock avalanche shows the importance of such attempts as they provide information on the recurrence of large slope failures and their associated hazards. Our effort stems from recognition, on 2005 satellite imagery, of topography and morphology indicative of a large, apparently pre-historic slope failure and the associated breached landslide dam, both features closely resembling the forms generated in the catastrophic 2008 earthquake. The follow-up reconstruction recognizes an earlier landslide deposit exhumed from beneath the 2008 Yangjia gully rock avalanche by fluvial erosion since May 2008. We infer a seismic trigger also for the pre-2008 rock avalanche based on the following circumstantial evidence: i) the same source area (valley-facing, terminal portion of a flat-topped, elongated mountain ridge) located within one and a half kilometer of the seismically active Beichuan fault; ii) significant directional amplification of ground vibration, sub-parallel to the failed slope direction, detected via ambient noise measurements on the ridge adjacent to the source area of the 2008 rock avalanche and iii) common depositional and textural features of the two landslide deposits. Then, we show how, through consideration of the broader geomorphic and seismo-tectonic contexts, one can gain insight into the spatial and temporal recurrence of catastrophic slope failures  in Beichuan County and elsewhere in the Longmen Shan. This insight, combined with local-scale geologic and geomorphologic knowledge, may guide selection of suspect slopes for reconnaissance, wide-area ambient noise investigation aimed at discriminating their relative susceptibility to co-seismic catastrophic failures. We indicate the feasibility of such investigations through the example of this study, which uses 3-component velocimeters designed to register low amplitude ground vibration.</p>


Author(s):  
Tim Davies

Rock avalanches are very large (greater than about 1 million m3) landslides from rock slopes, which can travel much farther than smaller events; the larger the avalanche, the greater the travel distance. Rock avalanches first became recognized in Switzerland in the 19th century, when the Elm and Goldau events killed many people a surprisingly long way from the origin of the landslide; these events first posed the “long-runout rock-avalanche” problem. In essence, the several-kilometer-long runout of these events appears to require low friction beneath and within the moving rock mass in order to explain their extremely long deposits, but in spite of intense research in recent decades this phenomenon still lacks a generally accepted explanation. Large collapses of volcano edifices can also generate rock avalanches that travel very long distances, albeit with a different runout–volume relationship to that of non-volcanic events. Even more intriguing is the presence of long-runout deposits not just on land but also beneath the sea and on the surfaces of Mars and the Moon. Numerous studies of rock avalanches have revealed a number of consistencies in deposit and behavioral characteristics: for example, that little or no mixing of material occurs within the moving debris mass during runout; that the deposit material beneath a meter-scale surface layer is pervasively and intensely fragmented, with fragments down to submicrometer size; that many of these fragments are agglomerates of even finer particles; that throughout the travel of a rock avalanche large volumes of fine dust are produced; that rock avalanche surfaces are typically covered by hummocks of a range of sizes; and that, as noted above, runout distance increases with volume. Since rock avalanches can travel tens of kilometers from their source, they pose severe, if low-probability, direct hazards to societal assets in mountain valleys; in addition, they can trigger extensive and long-duration geomorphic hazard cascades. Although large rock avalanches are rare (e.g., in a 10,000 km2 area of the Southern Alps in New Zealand, research showed that events larger than 5 × 107 m3 occurred about once every century), studies to date show that the proportion of total landslide volume involved in such large events is greater than the proportion in smaller, more frequent events, so that a large proportion of the total sediment generated in mountains by uplift and denudation originates in large rock avalanches. Consequently, large rock avalanches exert a significant influence on mountain geomorphology, for example by blocking rivers and forming landslide dams; these either fail, causing large dam-break floods and long-duration aggradation episodes to propagate down river systems, or remain intact to infill with sediment and form large valley flats. Rock avalanches that fall onto glaciers often result in large terminal moraines being formed as debris accumulates at the glacier terminus, and these moraines may have no relation to any climatic change. In addition, misinterpretation of rock avalanche deposits as moraines can cause underestimation of hazard risk and misinterpretation of paleoclimate. Rock avalanche runout behavior poses fundamental scientific questions, and rock avalanches have important effects on a wide range of geomorphic processes, which in turn pose threats to society. Better understanding of these impressive and intriguing events is crucial for both geoscientific progress and for reducing impacts of future disasters.


Author(s):  
Marten Geertsema ◽  
Alexandre Bevington

Large rock avalanches on glaciers are an annual occurrence in the mountains of western North America. Following an event, landslide investigators may strive to quickly arrive on site to assess the deposit. Satellite remote sensing imagery demonstrates that caution is warranted for on- site field assessments. We combine Landsat, Sentinel-1(radar), Sentinel-2 and Planet imagery to reconstruct the events of four recent double overlapping rock avalanche deposits in British Columbia. In our examples substantial precursory rock avalanches are closely followed (days - months) and buried by much larger landslides. We suggest that landslide investigators exercise caution when assessing fresh rock avalanches avalanche deposits in the field.


2020 ◽  
Vol 11 ◽  
pp. 374
Author(s):  
Masahito Katsuki ◽  
Yukinari Kakizawa ◽  
Akihiro Nishikawa ◽  
Yasunaga Yamamoto ◽  
Toshiya Uchiyama

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are needed for decision-making of the treatment. SAFIRE score using only four variables is a good prediction scoring system. However, making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on prediction models for SAH outcomes using DL. We herein made a prediction model using DL software, Prediction One (Sony Network Communications Inc., Tokyo, Japan) and compared it to SAFIRE score. Methods: We used 153 consecutive aneurysmal SAH patients data in our hospital between 2012 and 2019. Modified Rankin Scale (mRS) 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 102 patients training dataset and 51 patients external validation dataset. Prediction one made the prediction model using the training dataset with internal cross-validation. We used both the created model and SAFIRE score to predict the outcomes using the external validation set. The areas under the curve (AUCs) were compared. Results: The model made by Prediction One using 28 variables had AUC of 0.848, and its AUC for the validation dataset was 0.953 (95%CI 0.900–1.000). AUCs calculated using SAFIRE score were 0.875 for the training dataset and 0.960 for the validation dataset, respectively. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the model was not so inferior to those of previous statistically calculated prediction models.


2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.


2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.


2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.


Sign in / Sign up

Export Citation Format

Share Document