A time-space based approach for mapping rainfall-induced shallow landslide hazard

Xie, Mowen; Esaki, Taito; Cai, Meifeng

doi:10.1007/s00254-004-1069-1

Cited by 64 publications

(42 citation statements)

References 23 publications

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“…Test Condition A involves varied soil density by varying the number of compactions while maintaining the moisture content at 19% and the slope angle at 45°. e experiment intends to study the pore pressure development in the slope and its safety factor at di erent densities (1523, 1562, and 1592 kg/m 3 ). e densities were referred against the 98% of maximum dry density at 19% moisture content and were found to be 0% (loosest condition), 50% (medium dense), and 88% (very dense), respectively, in relative density.…”

Section: Resultsmentioning

confidence: 99%

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Landslide Susceptibility in Relation to Correlation of Groundwater Development and Ground Condition

Taib

Selaman

Chen

et al. 2017

Advances in Civil Engineering

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Rainfall is inevitably one of the main factors that trigger landslides. However, not much study has been conducted on the impact of groundwater rise on slope stability. us, this study is intended to focus on the rise of the groundwater level from the bottom of the slope which would lead to landslides due to pore pressure development by eliminating other landslide-triggering factors (i.e., in ltration and surface runo ). Saturated sand was used for slope modeling, and sand densities of 1523 kg/m 3 , 1562 kg/m 3 , and 1592 kg/m 3 were tested with a constant slope angle of 45°. Another set of experiments was also performed on slopes having angles of 25°, 45°, and 60°and with a maintained density of sand at 1562 kg/m 3 . rough observation, failure was initiated rst at the toe of the slope before minor and major slips or total collapse occurs. Dimensions of slip surfaces were measured and included in SLOPE/W for the computation of the safety factor. In conclusion, safety factors are found to be higher in denser soil and in the lowest slope angle. However, faster occurrence of collapse in denser soil was identi ed and could be contributed by the faster pore water pressure development.

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Section: Resultsmentioning

confidence: 99%

“…Several studies [2,3] suggest that precipitation is one of the important triggering factors that cause landslides. Gofar et al [4] stated that landslides induced by rainfall are most common in tropical regions where the climatic condition is governed by the wet and dry periods as experienced in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility in Relation to Correlation of Groundwater Development and Ground Condition

Taib

Selaman

Chen

et al. 2017

Advances in Civil Engineering

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“…Other conventional methods such as geotechnical model and the safety factor model have been applied in different areas (Gokceoglu et al 2000;Romeo 2000;Refice and Capolongo 2002;Carro et al 2003;Shou and Wang 2003;Zhou et al 2003;Youssef et al 2009). As a new approach to landslide susceptibility evaluation using GIS, fuzzy logic and artificial neural network models have been applied (Xie et al 2004;Ercanoglu and Gokceoglu 2002;Pistocchi et al 2002;Lee et al 2003aLee et al , b, 2004aTangestani 2004;Lee 2007b;Pradhan et al , 2009. has assessed landslide susceptibility analysis at Youngin, Korea using frequency ratio, logistic regression, and artificial neural network (ANN) model.…”

Section: Previous Workmentioning

confidence: 99%

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia

2009

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This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor's weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.

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“…In combination with a raster GIS, FoS calculations most commonly build on the assumption of a planar slope of infinite length, with the potential failure surface parallel to the topographic surface (infinite slope stability model; Van Westen and Terlien, 1996;Burton and Bathurst, 1998;Xie et al, 2004a;Baum et al, 2008;Godt et al, 2008;Mergili et al, 2014;Raia et al, 2014). However, the infinite slope stability model is well suited only for shallow slope stability in frictional materials, and is less appropriate for cohesive materials (Mergili et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A strategy for GIS-based 3-D slope stability modelling over large areas

et al. 2014

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Abstract. GIS-based deterministic models may be used for landslide susceptibility mapping over large areas. However, such efforts require specific strategies to (i) keep computing time at an acceptable level, and (ii) parameterize the geotechnical data. We test and optimize the performance of the GISbased, 3-D slope stability model r.slope.stability in terms of computing time and model results. The model was developed as a C-and Python-based raster module of the open source software GRASS GIS and considers the 3-D geometry of the sliding surface. It calculates the factor of safety (FoS) and the probability of slope failure (P f ) for a number of randomly selected potential slip surfaces, ellipsoidal or truncated in shape. Model input consists of a digital elevation model (DEM), ranges of geotechnical parameter values derived from laboratory tests, and a range of possible soil depths estimated in the field. Probability density functions are exploited to assign P f to each ellipsoid. The model calculates for each pixel multiple values of FoS and P f corresponding to different sliding surfaces. The minimum value of FoS and the maximum value of P f for each pixel give an estimate of the landslide susceptibility in the study area. Optionally, r.slope.stability is able to split the study area into a defined number of tiles, allowing parallel processing of the model on the given area. Focusing on shallow landslides, we show how multi-core processing makes it possible to reduce computing times by a factor larger than 20 in the study area. We further demonstrate how the number of random slip surfaces and the sampling of parameters influence the average value of P f and the capacity of r.slope.stability to predict the observed patterns of shallow landslides in the 89.5 km 2 Collazzone area in Umbria, central Italy.

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A time-space based approach for mapping rainfall-induced shallow landslide hazard

Cited by 64 publications

References 23 publications

Landslide Susceptibility in Relation to Correlation of Groundwater Development and Ground Condition

Landslide Susceptibility in Relation to Correlation of Groundwater Development and Ground Condition

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia

A strategy for GIS-based 3-D slope stability modelling over large areas

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