Landslide Susceptibility Assessment using Frequency Ratio Model Applied to an Area along the E-W Highway (Gerik-Jeli)

Abstract

Problem statement:The study area is frequently subjected to landslides especially alongside the E-W high way. This study demonstrates the implementation of the Frequency Ratio (FR) model with the aid of GIS tool to assess the contribution of every predisposing factor on landslides and produce a landslide susceptibility map to the area. Approach: A landslide location map of the study area was prepared based on the interpretation of aerial photographs, previous landslide maps and field surveys. In addition, ten relevant thematic maps representing considered factors were extracted from the constructed spatial database. Slope gradient, slope aspect, elevation, distance to road and drainage density, all were calculated from the topographic database; lithology, strata dip map and foliation dip map were generated from the geologic database; lineament density was prepared from Landsat-7 (ETM+) satellite image; and a soil map was prepared from the soil database. Results: relationship between landslides and instability factors was statistically evaluated by frequency ratio analysis. The results suggested that distance to road, lineament density and slope gradient are the most important factors effecting landslides. Frequency ratio values were used to produce the Landslide Susceptibility Index (LSI) with which the study area was divided into five zones of relative landslide susceptibility. Conclusion: The results of the analysis have been validated by calculating the AUC which shows an accuracy of 88.31% in the case of success rate curve and 84.68 % in the case of the prediction rate curve, indicating a high quality susceptibility map obtained from the FR model. The map could be used by decision makers as basic information for slope management and land use planning.

A new region-based preparatory factor for landslide susceptibility models: the total flux

“…The percentage of landslide occurrence (POLO) and its cumulative (CPOLO) are commonly used as a good indicator to measure the performance of LSM: The steeper the CPOLO curve, the better the capability of LSM to predict landslides and to validate LSMs (e.g., Chung and Fabbri 2003;Frattini et al 2010;Mezughi et al 2011;Oh and Lee 2011). We follow this procedure to derive a success rate curve by calculating the LSM of all cells using Eq.…”

Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

“…The SINMAP method allows the determination of the stability of an area regarding landslide susceptibility and slope instability (Akgun & Erkan, ; Nery & Vieira, ). The area susceptible to landslide hazards is classified into different classes based on the FS values (Table ) (Akgun & Erkan, ; Mezughi, Akhir, Rafek, & Abdullah, ).…”

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

“…In bivariate analysis, each individual landslide-influencing factor is combined with a landslide inventory map, and weight values based on landslide densities are calculated for its corresponding classes. A well-known and widely used bivariate analysis method for LS zonation is frequency ratio calculation, often referred as the landslide susceptibility index (LSI) [26,27,[30][31][32][33][34]. Tien Bui et al [35] evaluated and compared the results of applying the LSI and the (multivariate) logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam.…”