Probabilistic Landslide Hazard Assessment using Statistical Information Value (SIV) and GIS Techniques

Sharma, Ankit; Sur, Ujjwal; Singh, Prafull; Kumar, Praveen; Srivastava, Prashant K.

doi:10.1002/9781119359203.ch15

Cited by 17 publications

(4 citation statements)

References 47 publications

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“…The soil suitability layer, which presented complexes of agricultural soil suitability, was created based on the digitalization of the soil suitability map. curvature DEM ArcGIS Natural Jenks [3,13,15,16,18,20,21,24] side exposure index (SEI) DEM [47] Natural Jenks [45] tectonics geology map ---- [3,13,14,19,20,22,48] lithostratigraphic unit geology map ---- [19,27] fault proximity geology map EDB Natural Jenks [13,[19][20][21][22]24,26] thrust proximity geology map EDB Natural Jenks [49,50] distance to streams DEM EDB Natural Jenks [3,13,19,20] distance to lake Ro żnów lake shapefile EDB Natural Jenks [16,24] compound topographic index (CTI) DEM [47] Natural Jenks [13,48,51,52] integrated moisture index (IMI) DEM [47] Natural Jenks [46,53] flow direction DEM [47] Natural Jenks [45,54] precipitation measurements from meteorological...…”

Section: Preparation Of Landslide Conditioning Factorsmentioning

confidence: 99%

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020

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To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

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Section: Preparation Of Landslide Conditioning Factorsmentioning

confidence: 99%

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

2020

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“…Often, these technologies combine various datasets collected from innovative techniques such as satellite remote sensing and light detection and ranging (LiDAR) images (Roccati et al, 2021). These technologies are widely integrated with numerous statistical models and approaches which have been suggested and extensively used in landslides susceptibility mapping as engaged in various studies globally including; frequency ratio (FR) (Aditian et al, 2018;Silalahi et al, 2019;Yalcin et al, 2011) index of entropy Singh et al, 2021), analytical hierarchy process (AHP) (Jam et al, 2021;Moragues et al, 2021), analytical network process (ANP) (Hamzeh & Amiri, 2020; Swetha & Gopinath, 2020), information value (Sharma et al, 2020;Wubalem & Meten, 2020), fuzzy logic (Baharvand et al, 2020;Bahrami et al, 2020) among others and a hybrid of the aforementioned techniques can be used to overcome biasines (Gheshlaghi & Feizizadeh, 2021). However, for regional and small scale landslides susceptibility mapping, statistical methods are the most and widely frequently adopted techniques (Cascini, 2008;Yalcin et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

Makonyo

Zahor

2023

Nat Hazards

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Landslides are becoming increasingly widespread, claiming tens of thousands of fatalities, hundreds of thousands of injuries, and billions of dollars in economic losses each year. Thus, studies for geographically locating landslides vulnerable areas have been increasingly relevant in recent decades. This research is aimed at integrating Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to delineate Landslide Susceptibility Mapping (LSM) of Lushoto District, Tanzania. RS assisted in providing remotely datasets including; Digital Elevation Models (DEM), Landsat 8 OLI imageries, and spatially distributed landslides coordinates with the use of a handheld Global Position System (GPS) receiver while various GIS analysis techniques were used in the preparation and analysis of landslides in uencing factors hence, generating LSM index values. However, rainfall, slope's angle, elevation, soil type, lithology, proximity to roads, rivers, faults, and Normalized Difference Vegetation Index (NDVI) factors were found to have direct in uence on the occurrence of landslides. These factors were evaluated, weighted, and ranked using Analytical Hierarchy Process (AHP) technique in which 0.086 (8.6%) consistency ratio (CR) was attained (highly accepted). Findings reveal that, rainfall (29.97%), slopes' angle (21.72%), elevation (15.68%), and soil types (11.77%) were found to have high in uence on the occurrence of landslides while proximity to faults (8.35%), lithology (4.94%), proximity to roads (3.41%), rivers (2.48%) and NDVI (1.69%) had very low in uences respectively. The overall results, obtained through Weighted Linear Combination (WLC) analysis indicate that, about 97669.65 hectares (ha) of the land is under very low landslides susceptibility levels which accounts for 24.03% of the total study area. Low susceptibility levels had 123105.84 ha (30.28%) moderate landslides susceptibility areas were found to have 140264.79 ha (34.50%) while high and very high susceptibility areas were found to cover about 45423.43 ha (11.17%) and 57.78 ha (0.01%) respectively. Furthermore, 81% overall model accuracy was obtained as computed from Area under the Curve (AUC) using Receiver Operating Characteristic (ROC) Curve.

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“…This is because the intention is to analyze how earthquakes and rainfall can trigger landslides. The main intention of this paper is to contribute and to encourage civil engineers to use probability in geotechnical engineering and to raise awareness about the need to work with numerical methodologies for the assessment of landslide hazard instead of using qualitative methods that do not actually give precise results [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“Probabilistic Landslide Hazard Methodology, an Application to a Susceptible Area to Landslides in Colombia”

Yepes-Heredia¹

2021

J Civ Eng Res Technol

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A Probabilistic method was previously used to perform Probabilistic Hazard Zonation in El Salvador using a new proposed probabilistic methodology (Rodriguez, Yepes 2011). The current project is a case study that uses the same methodology and tries to cover the limitations of the previous and first application, and applied to two historically unstable landslides in Pipiral, an unstable area in the Central Region of Colombia. The susceptibility angle was used as the susceptibility function. Rainfall and earthquakes are considered as landslides triggers. Besides zonation, modeling was performed because the probability model was initially designed to do the zonation of larger areas. A database from “four countries in Central-America and Colombia” of Rainfall Induced Landslides in Fine-grained soils and a database of “historic and worldwide” Earthquake Induced Landslides, were considered to support the model. The intensity-duration-frequency (I-D-F) curves for the Pipiral-Colombia were used to define the probability of occurrence of the critical rainfall, and the seismic hazard analysis of the same area was used to define the probability of occurrence of the critical earthquake.

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Probabilistic Landslide Hazard Assessment using Statistical Information Value (SIV) and GIS Techniques

Cited by 17 publications

References 47 publications

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania

“Probabilistic Landslide Hazard Methodology, an Application to a Susceptible Area to Landslides in Colombia”

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