Local rainfall thresholds for forecasting landslide occurrence: Taipingshan landslide triggered by Typhoon Saola

Chung, Ming-Chien; Tan, Chih-Hao; Chen, Chien-Hsin

doi:10.1007/s10346-016-0698-2

Cited by 47 publications

(14 citation statements)

References 23 publications

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“…Every year, landslides cause substantial human deaths and mass economic loss worldwide (Beyabanaki et al 2016;Carlini et al 2016). Many scientists are engaged in research on landslide disasters; the key question is when and where it will happen and what can be done (Chou et al 2017;Chung et al 2017). Landslide susceptibility modelling is the most commonly used method to identify and predict landslides (Ciurleo et al 2016;Conte et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Hong

Pradhan

Sameen

et al. 2017

Geomatics, Natural Hazards and Risk

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This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index (NDVI), landuse, rainfall, distance to road, distance to river, distance to fault, plan curvature, and profile curvature, were analyzed. Chi-square feature selection method was adopted to compare the significance of each factor with landslide occurence. The GWR model was compared with two well-known models, namely, logistic regression (LR) and support vcector machine (SVM). Results of chi-square feature selection indicated that lithology and slope are the most influencial factors, whereas SPI was found statistically insignificant. Four landslide susceptibility maps were generated by GWR, SGD-LR, SGD-SVM, and SVM models. The GWR model exhibited the highest performance in terms of success rate and prediction accuracy, with values of 0.789 and 0.819, respectively. The SVM model exhibited slightly lower AUC values than that of the GWR model. Validation result of the four models indicates that GWR is a better model than other widely used models.

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Section: Introductionmentioning

confidence: 99%

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Hong

Pradhan

Sameen

et al. 2017

Geomatics, Natural Hazards and Risk

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“…This study uses the GeoStudio software to perform slope stability analysis (Chung et al, 2017) on a two dimensional model of the Chashan site. In GeoStudio software, the limit equilibrium method module SLOPE/W was used.…”

Section: Scenario Simulationmentioning

confidence: 99%

Failure Mechanism Analysis Considers the Hydrological and Seismic Conditions at Chashan Site in Central Taiwan

Chung¹,

Chen²,

Tai

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The investigation results of large-scale landslides in the central part of Taiwan from Central Geologic Survey identified a new, largescale landslide in the Chashan Village of Alishan Township, Chiayi County (ID: D160) which has the potential to influence the safety of Chashan Village residents. In coordination with the surface geological survey, Chashan site can be divided into three potential sliding areas (zones A, B and C). The paper uses GeoStudio for evaluating the stability of different hydrological and seismic scenarios. The results of scenario simulation show that: (1) In the normal and high water level scenarios, the safety factor obtained in zone A is greater than 1.0, and the initial judgment belongs to the stable state; (2) In the earthquake scenarios, the A4 potential sliding mass in zone A will be collapsed; (3) Through the scenario simulation, when the water level in zone A rises more than 25 meters or the PGA exceeds 160 gal, it is possible to cause the collapse of A4 potential sliding mass. The result of the study will contribute that the complex phenomenon between the stability of landslides and hydrogeological conditions can be revealed and clarified through the detailed study on the hydrogeological investigation, observation and the numerical simulation.</p>

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“…Slope/W is a slope stability modeling component of the software package Geostudio that is licensed for engineering consulting use (Geostudio, 2016). In Chung et al (2017), Slope/W was used to model landslide sensitivity to precipitation and identify a landslide activity rainfall threshold.…”

Section: Landslide Sensitivity To Scour and Pore-water Pressurementioning

confidence: 99%

Fluvial Connectivity of a Deep-Seated Landslide to Upstream Tree Harvests

Keck¹

2017

Geological Society of America Abstracts With Programs

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Channel wall scour may have debuttressed a deep-seated landslide located in a small, mountainous drainage in the Clearwater River Watershed in the western Olympic Mountains, Washington State. Debris flows and high peak flows, both of which can cause channel wall scour, may have been caused by upstream tree harvests. Through examination of stream flow, debris flow and precipitation records relative to the tree harvest and deep-seated landslide activity records, tree harvest effects on the deep-seated landslide are clarified. Following harvest on the landslide, evidence of deep seated landslide activity is undetectable until the trees upstream of the landslide are harvested. In total, three periods of landslide activity are observed. High stream flow (a 25-year event)coincides with one period of activity; however, the magnitude of flow events larger than a 1.1-year event are unaffected by tree harvests. High precipitation and snow melt (water input) events coincide with two of the landslide events but at the time of landslide activity, evapotranspiraiton rates of the plantation trees may have been nearly equal to that of the original forest. In contrast, debris flows, which coincide with all periods of deep-seated landslide activity, dramatically increase following harvest in the headwaters despite below average annual maximum 1-day precipitation and no change in annual maximum 30-day precipitation. Tree harvests in the headwaters of the watershed appear to have caused the increase in debris flow frequency which in turn triggered landslide activity.iii

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Local rainfall thresholds for forecasting landslide occurrence: Taipingshan landslide triggered by Typhoon Saola

Cited by 47 publications

References 23 publications

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

Failure Mechanism Analysis Considers the Hydrological and Seismic Conditions at Chashan Site in Central Taiwan

Fluvial Connectivity of a Deep-Seated Landslide to Upstream Tree Harvests

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