Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

Landslides (Impact Factor: 2.81). 09/2011; 8(3):293-308. DOI: 10.1007/s10346-011-0257-9

ABSTRACT The increased socio-economic significance of landslides has resulted in the application of statistical methods to assess their
hazard, particularly at medium scales. These models evaluate where, when and what size landslides are expected. The method presented in this study evaluates the landslide hazard on the basis of homogenous susceptible
units (HSU). HSU are derived from a landslide susceptibility map that is a combination of landslide occurrences and geo-environmental
factors, using an automated segmentation procedure. To divide the landslide susceptibility map into HSU, we apply a region-growing
segmentation algorithm that results in segments with statistically independent spatial probability values. Independence is
tested using Moran’s I and a weighted variance method. For each HSU, we obtain the landslide frequency from the multi-temporal data. Temporal and
size probabilities are calculated using a Poisson model and an inverse-gamma model, respectively. The methodology is tested
in a landslide-prone national highway corridor in the northern Himalayas, India. Our study demonstrates that HSU can replace
the commonly used terrain mapping units for combining three probabilities for landslide hazard assessment. A quantitative
estimate of landslide hazard is obtained as a joint probability of landslide size, of landslide temporal occurrence for each
HSU for different time periods and for different sizes.


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Available from: Alfred Stein, Aug 27, 2015
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    • "In addition, extensive human interference in hill slope areas for the construction of roads, urban expansion along the hill slopes, deforestation and rapid change in land use contribute to instability. This makes it difficult, if not impossible, to define a single methodology to identify and map landslides, to ascertain landslide hazards and to evaluate the associated risk (Guzzetti et al. 2005; Das et al. 2011). In this study, topography, geology, climate, vegetation and anthropogenic factors were selected based on expert knowledge, on the basis of field studies related to active landslides. "
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    ABSTRACT: The GIS-multicriteria decision analysis (GIS-MCDA) technique is increasingly used for landslide hazard mapping and zonation. It enables the integration of different data layers with different levels of uncertainty. In this study, three different GIS-MCDA methods were applied to landslide susceptibility mapping for the Urmia lake basin in northwest Iran. Nine landslide causal factors were used, whereby parameters were extracted from an associated spatial database. These factors were evaluated, and then, the respective factor weight and class weight were assigned to each of the associated factors. The landslide susceptibility maps were produced based on weighted overly techniques including analytic hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA). An existing inventory of known landslides within the case study area was compared with the resulting susceptibility maps. Respectively, Dempster-Shafer Theory was used to carry out uncertainty analysis of GIS-MCDA results. Result of research indicated the AHP performed best in the landslide susceptibility map-ping closely followed by the OWA method while the WLC method delivered significantly poorer results. The resulting figures are generally very high for this area, but it could be proved that the choice of method significantly influences the results.
    Natural Hazards 01/2013; 2013(65):2105 – 2128. DOI:10.1007/s11069-012-0463-3 · 1.96 Impact Factor
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    • "For estimation of the spatial probability of landslide hazards, various methods and models are successfully developed and used in the literature (Chacon et al. 2006; Guzzetti et al. 2006; Yao et al. 2008; Pradhan and Lee 2010; Pradhan et al. 2010; Yeon et al. 2010; Yilmaz 2010; Marjanovic et al. 2011; Oh and Pradhan 2011; Sezer et al. 2011; Althuwaynee et al. 2012; Ballabio and Sterlacchini 2012; Devkota et al. 2012; Lee et al. 2012; Pourghasemi et al. 2012a; 2012b; Xu et al. 2012; Zare et al. 2012; Tien Bui et al. 2012c; Pradhan 2010a, b, 2011a, b, 2012). However, few attempts have been carried out to estimate temporal probability of slope failure (Guzzetti et al. 2005; Jaiswal et al. 2010; Das et al. 2011). Thus, landslide hazard mapping is considerably challenging either due to incomplete dataset or unavailability of historical data in developing countries (Harp et al. 2009) such as in Vietnam. "
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    ABSTRACT: The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.
    Natural Hazards 03/2012; 66(2):1-24. DOI:10.1007/s11069-012-0510-0 · 1.96 Impact Factor
  • Landslides 06/2013; 10(3). DOI:10.1007/s10346-013-0405-5 · 2.81 Impact Factor
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