Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China

Zhuang, Jian; Peng, Jianbing; Iqbal, Javed; Liu, Tieming; Liu, Na; Li, Yazhe; Ma, Penghui

doi:10.1007/s11707-014-0474-3

Cited by 21 publications

(11 citation statements)

References 33 publications

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“…This is consistent with Ohlmacher (2007) planar plan curvature has the highest probability for landslides in regions dominated by earth flows and earth slides in clayey soils. Zhuang et al (2015) also observed that landslide occurrences are abundant at locations with convex (40.96%) and concave surfaces (36.75%) in the Qin Mountain, with planar surfaces (42.86%) in the Li Mountain, and convex (40.82%) and planar (34.70%) surfaces in the Loess Tableland. Regmi et al (2014) observed that debris flows mostly occur in areas of topographic convergence covered by unconsolidated deposits, whereas debris slides mostly occur in planar and concave slopes covered by unconsolidated alluvium and colluvium.…”

Section: Curvaturementioning

confidence: 75%

“…As revealed in Figs. 5, 6 and Table 2 Zhuang et al (2015) found that the highest density of landslides (51.81%) was in a higher slope range of 15°-40°. The slope gradient controls the shear forces acting on the hillside (Silalahi et al 2019) and thus steeper slopes are more prone.…”

Section: Slope Anglementioning

confidence: 87%

“…Generally, in the current study area, the distribution of landslides was infrequent on the lower and much steeper slopes. As explained further by Zhuang et al (2015) on lower slope gradients, the density of landslides is low because the terrain is gentle and covered with thick colluvium and/or residual soils which require higher water levels to initiate slope failures. Correspondingly, at very high slope gradients, the density of landslides is also low because the terrain is very steep with a small amount of material (e.g.…”

Section: Slope Anglementioning

confidence: 99%

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Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda

Nakileza

Nedala

2020

Geoenviron Disasters

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This study aimed to characterize the topographic effect on landslides attributes and explore the implications on risk management in a tropical mountainous environment. A database was constructed based on landslide inventory from field surveys supplemented by desk research. The topographic parameters were derived from STRM DEM of a 30 m resolution for the study area. The analysis of the data was conducted in Arc GIS 10.5 environment. The relationship between landslides and topographic conditioning factors was analysed using the Frequency Ratio model. Results revealed that most landslides were distributed within the altitudinal range of 1500 to 1800 m a.s.l. on moderately steep slopes (15 o-20o) in concave curvatures (hollows). Shallow slides mainly debris flows and debris slides were predominant. Most slope failures were initiated on mid to upper slope positions in either new or old scars. Some runout depositions of large slides ended in streams thus undermining water quality. The findings on topographic parameters have implications and yet landslide risk management by the local population was generally inadequate. Any efforts toward effective landslide risk management should prioritise greening the sensitive topographic hollows and old scars particularly on mid to upper slope positions.

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Section: Curvaturementioning

confidence: 75%

Section: Slope Anglementioning

confidence: 87%

Section: Slope Anglementioning

confidence: 99%

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Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda

Nakileza

Nedala

2020

Geoenviron Disasters

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“…A way to reduce bias is to include statistical approaches (Fall et al 2006). Since such methods are also relatively easy to implement in geographical information systems (GIS) (Kawasaki et al 2001;Pradhan 2013), an increasing number of studies have adopted statistical approaches such as bivariate and multivariate analysis including logistic regression, for landslide susceptibility mapping (Zhuang et al 2015;Nefeslioglu et al 2008;Akgun et al 2011;Mancini et al 2010;Althuwaynee et al 2014;Schicker and Moon 2012;Youssef et al 2015;Bui et al 2011;Hina et al 2014). Many non-deterministic models are designed to overcome the complexity of landslide susceptibility mapping (matter-element model, fuzzy set methods, artificial neural network, fuzzy models) and have been employed in several studies concerning landslide hazard assessment (Wu et al 2003;Bui et al 2012;Pradhan 2011a, b;Shahabi et al 2012;Zare et al 2013;Wu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping of the Sera River Basin using logistic regression model

et al. 2016

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Of the natural hazards in Turkey, landslides are the second most devastating in terms of socioeconomic losses, with the majority of landslides occurring in the Eastern Black Sea Region. The aim of this study is to use a statistical approach to carry out a landslide susceptibility assessment in one area at great risk from landslides: the Sera River Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical approach in the form of a logistics regression model to explore the probability distribution of future landslides in the region. The model attempts to find the best fitting function to describe the relationship between the dependent variable, here the presence or absence of landslides in a region and a set of independent parameters contributing to the occurrence of landslides. The dependent variable (0 for the absence of landslides and 1 for the presence of landslides) was generated using landslide data retrieved from an existing database and expert opinion. The database has information on a few landslides in the region, but is not extensive or complete, and thus unlike those normally used for research. Slope, angle, relief, the natural drainage network (including distance to rivers and the watershed index) and lithology were used as independent parameters in this study. The effect of each parameter was assessed using the corresponding coefficient in the logistic regression function. The results showed that the natural drainage network plays a significant role in determining landslide occurrence and distribution. Landslide susceptibility was evaluated using a predicted map of probability. Zones with high and medium susceptibility to landslides make up 38.8 % of the study area and are located mostly south of the Sera River Basin and along streams.

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“…Landslide (mass movements) acts on natural and engineered slopes in steep topography [1] [2]. Rainfall and Earthquakes are main triggering factor for mass movements [3] [4] [5] [6], however the rock type, type of bedrock, type of cover material, geotechnical and hydrogeological properties, land use, slope gradient, slope shape, and so on can be considered as contributory other contributory factors [3] [7] [8] [9] [10].…”

Section: Introductionmentioning

confidence: 99%

A Rainfall Intensity-Duration Threshold for Mass Movement in Badulla, Sri Lanka

Perera¹,

Jayawardana²,

Jayasinghe³

2017

GEP

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Mass movement in Sri Lanka is mainly triggered by heavy rainfall. International literature is rich of works defining rainfall intensity-duration models to identify the rainfall threshold for various types of Mass movement. However, studies have not focused to establish a relationship between intensity and duration of rainfall in Sri Lanka. Therefore, this study focused to establish rainfall intensity-duration models to identify the rainfall threshold for mass movements in Badulla district in Sri Lanka, where forty four (44) rainfall events that resulted in same number of landslides during the last three decades were considered. Results indicate the rainfall threshold relationship fits to the log linear model of the exponential function, I = α·D −β . The constructed I-D curve revealed that short duration (<2 h) and high-intensity (>54 mm/h) in rainfall events can potentially trigger the landslide. However, long-duration (>8 h) and low-intensity (<25 mm/h) in rainfall events may also trigger mass movements in Badulla. As per the results, most mass movements occur during northeast monsoons and inter-monsoons. In general, higher mean rainfall intensities trigger the debris flows, while long-duration rainfall events can trigger both landslides and debris flow. When compared to Sri Lankan mass movements triggering threshold intensities are fairly higher than the global threshold values. It confirms that within Badulla, mass movements are triggered by very high intense and/or long duration rainfalls events only. Further, time series analysis of the rainfall events shows an upward trend of extreme rainfall events, which increased landslide occurring frequency in last six (6) years.

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Identification of landslide spatial distribution and susceptibility assessment in relation to topography in the Xi’an Region, Shaanxi Province, China

Cited by 21 publications

References 33 publications

Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda

Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda

Landslide susceptibility mapping of the Sera River Basin using logistic regression model

A Rainfall Intensity-Duration Threshold for Mass Movement in Badulla, Sri Lanka

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