Spatial Modeling in Landslide Susceptibility

Gunadi, Dwi Shanty Apriliani; Jaya, Indra; Tjahjono, Budi

doi:10.11591/ijeecs.v5.i1.pp139-146

Cited by 4 publications

(2 citation statements)

References 4 publications

(4 reference statements)

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“…Shahabi et al [44] applied FR, AHP and LR methods for LSM and the models have well predicted whether LR model has the high prediction value. Now quantitative analyses of geomorphological parameters have been performed by principal component analysis (PCA) in a scientific research such as landslide vulnerability [45]. PCA is a statistical method used to reduce a large number of interrelated variables to a smaller number of dominant variables for landslide occurrence or landslide susceptibility [46].…”

Section: Introductionmentioning

confidence: 99%

GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India

2019

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Landslide causes damage to property and life in the Himalayan region as well as Sikkim Himalaya. Landslide susceptibility assessment is very important to mark out the landslide susceptibility area, and researchers take some plans for the future. Landslide susceptibility map has become essential to identify the landslide-prone zones and to find out the probable causes of a landslide in an area. The main objectives of this study are to produce landslide susceptibility mapping by frequency ratio method and to find out the dominant parameters which are responsible for the occurrences of frequent landslide in Lachung River basin, the main tributary of Teesta River in Sikkim Himalaya. The study utilized different types of data which include geological data, advanced spaceborne thermal emission and reflection radiometer-based digital elevation model, Sentinel-2A sensor data, published thematic map and precipitation data, and all data have been processed with the help of remote sensing and GIS tools. Ten influential causative factors of landslide occurrence are used for the susceptibility assessment, and they are slope angle, slope aspect, elevation, profile curvature, land use/land cover, normalized differences vegetation index, drainage density, road density, geology and rainfall. The GIS-based landslide susceptibility analysis has been discussed with ten dominant factors by using frequency ratio model. Finally, the landslide susceptibility map was classified into very high (0.591%), high (1.867%), moderately high (5.172%), moderate (19.682%), moderately low (25.685%), low (29.816%) and very low (17.187%). The map was compared with the validation of landslide location, and the model was verified by the receiver operating characteristic curve. The results revealed 88.9% prediction rate and 92.3% success rate, which means this model is validated with landslide susceptibility analysis in the study area.

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Section: Introductionmentioning

confidence: 99%

GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India

2019

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“…in the form of maps and coordinates in two or three-dimensional spaces [11]. The spatial effects in data attributes that geographically have relevance must be identified based on the spatial pattern description and relationships [11,12]. The spatial effects cause the appearance of spatial dependence.…”

Section: Introductionmentioning

confidence: 99%

Development of a Spatial Path-Analysis Method for Spatial Data Analysis

Sulistyo¹,

Subanar²,

Pulungan³

2018

IJECE

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Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.

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