High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia

Abstract

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

“…Several studies have shown different results on the factors that cause landslides. These studies showed slope as a significant factor (Okoli et al, 2023;Pamela et al, 2018), but other studies that also used slope did not show the most significant results among the factors used such as elevation (Ling et al, 2022;Pourghasemi et al, 2020); distance to road (Wang et al, 2019;Zhao et al, 2024); fault (Guo et al, 2021), landuse (Hemasinghe et al, 2018) and manymore.…”

Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province

“…The Landslide Susceptibility Index (LSI) is calculated by the sum of each factor ratio value using the equation (Eq.2): LSI = Fr1 + Fr2 + Fr3 + Frn……………..Eq.2] LSI will be obtained by adding up each FR value for each conditioning factor correlated with landslide history, where FR is the level of each type of factor. Landslide vulnerability maps are obtained from the correlation between the factors overlaid using spatial weight overlay analysis (Okoli et al, 2023).…”

Hazards profile of the Shigar Valley, Central Karakoram, Pakistan: Multicriteria hazard susceptibility assessment

“…Curvature is classified into three classes: concave slope (negative value), flat plane (value −0.1 to 1.0), and convex slope (value greater than 0.1) (Gizaw et al 2023). The stream power index calculates the erosive force of water in rivers or streams which was also extracted from the DEM (Okoli et al 2023;Olii et al 2023). Likewise, flow accumulation was extracted from DEM.…”