Assessment of regional rainfall-induced landslides using 3S-based hydro-geological model

Tan, Chih-Hao; Ku, Cheng-Yu; Chi, Shu-Yeong; Chen, Y; Fei, L; Lee, J; Su, Tzuoh-Ying

doi:10.1201/9780203885284-c225

Cited by 11 publications

(11 citation statements)

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“…1. The same observation was made by (Tan et al, 2008;Salciarini et al, 2006;De Rose, 2009). The result of field investigation in this study has confirmed to the general observation being cited in the literature that soil thickness decreases with increasing slope angle.…”

Section: Correlation Of Soil Thickness With the Slope Anglesupporting

confidence: 74%

Empirical models for predicting the spatial variation of soil thickness and shear strength for landslide susceptibility assessment

Daleon

Lorenzo

2018

Journal of Nepal Geological Society

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Spatial variation of soil thickness, cohesion and friction angle are crucial in large scale slope stability analysis. However, determination of these parameters is challenging, costly and time consuming. In order to opt out expensive laboratory tests and laborious field investigation, efforts had been done by many researchers to predict these parameters over large areas. In this study, the soil samples and soil thickness data are gathered from 30 sampling boreholes and 60 probed points in an area with Pliocene-Pleistocene geology. The details on slope angle are obtained from the slope map through a processed Digital Elevation Model (DEM) in Geographic Information System (GIS) particularly the ArcGIS software. Slope angles are confirmed in the site through actual measurement using the clinometer. The soil thickness is measured from the ground surface down to the interface of soil and weathered bedrock. The cohesion and friction angle are determined from the direct shear test. Regression analysis is used to establish an empirical model correlating the soil thickness and shear strength parameters with the slope angle. The result revealed that the slope angle has yielded better correlation to the soil thickness than the cohesion and friction angle. The resulting slope angle-soil thickness empirical model is validated from ten different sites with similar geology.

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Section: Correlation Of Soil Thickness With the Slope Anglesupporting

confidence: 74%

Empirical models for predicting the spatial variation of soil thickness and shear strength for landslide susceptibility assessment

Daleon

Lorenzo

2018

Journal of Nepal Geological Society

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“…This underestimation is probably due to the model's assumptions, which are discussed in the next section. Table 2 Input values of parameters for the critical rainfall model (after Tsai, 2007 andTan et al, 2008). …”

Section: Model Application and Validationmentioning

confidence: 99%

“…This study derived landslide volume by multiplying the number of unstable cells by the average soil depth of 2 m. It may lead to overestimates on steep slopes and underestimates on gentle slopes. There are at least two options for improving the estimation: (1) use a soil map with depth measures or a slope dependent function (e.g., Tan et al, 2008), rather than an average soil depth, as input data; and (2) estimate landslide volume from landslide area based on an empirical relationship (e.g., the power law) developed from a multi-temporal landslide inventory (Guzzetti et al, 2009). These two options, however, were not available to us for this study; they will be pursued in future studies.…”

Section: Estimation Of Landslide Volumementioning

confidence: 99%

Simulation of event-based landslides and debris flows at watershed level

et al. 2012

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“…Most of these studies were conducted in countries with a high risk of rainfall-induced landslides, such as the mountainous areas of Southeast Asia (including Hong Kong and Taiwan), which experience frequent shallow landslides initiated by heavy rainfall in the typhoon season [26]. Assessment of regional rainfall-induced landslides using 3S-based hydro-geological model [28] Taiwan, Ta Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach [29] Taiwan, Route Nantou 71 Bet. Rainfall infiltration: infinite slope model for landslides triggering by rainstorm [26] Hong Kong, Tung Chung East Lantau Island Soil type 2010…”

Section: Introductionmentioning

confidence: 99%

“…GIS can use multi-layered analysis and artificial networks in order to evaluate slope stability and landslide risk at various scales [28]. Conventional analyses of rainfall-caused landslide risks have essentially relied on either two-dimensional numerical analyses with limit balances or determinate elements and different analysis systems [30].…”

Section: Introductionmentioning

confidence: 99%

Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

Kim

Chae

et al. 2018

Sustainability

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Landslide susceptibility models are important for public safety, but often rely on inaccessible or unaffordable software and geospatial data. Thus, affordable and accessible landslide prediction systems would be especially useful in places that lack the infrastructure for acquiring and analyzing geospatial data. Current landslide susceptibility models and existing methodologies do not consider such issues; therefore, this study aimed to develop an accessible and affordable landslide susceptibility modeling application and methodology based on open-source software and geospatial data. This model used TRIGRS (asc format) and QGIS (Digital Elevation Models (DEMs) extracted from GeoTIFF format) with widely accessible environmental parameters to identify potential landslide risks. In order to verify the suitability of the proposed application and methodology, a case study was conducted on Lantau Island, Hong Kong to assess the validity of the results, a comparison with 1999 landslide locations. The application developed in this study showed a good agreement with the four previous landslide locations marked as highly susceptible, which proves the validity of the study. Therefore, the developing model and the cost-effective approach, in this study simulated the landslide performance well and suggested the new approach of the landslide prediction system.

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Assessment of regional rainfall-induced landslides using 3S-based hydro-geological model

Cited by 11 publications

References 0 publications

Empirical models for predicting the spatial variation of soil thickness and shear strength for landslide susceptibility assessment

Empirical models for predicting the spatial variation of soil thickness and shear strength for landslide susceptibility assessment

Simulation of event-based landslides and debris flows at watershed level

Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

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