Assessing the impact of RCP4.5 and RCP8.5 scenarios on landslide susceptibility mapping using support vector machine: A case study of Penang Island, Malaysia

Dam hazards impose huge risks to the community as well as infrastructures. Obtaining and comprehending terrain features through geomorphological mapping is vital for dam area as it enables prediction of potential future terrain changes. The utilization of Unmanned Aerial Vehicles (UAVs) has garnered significant interest in geological, geomorphological, and geotechnical studies owing to their capacity to acquire high-resolution data from challenging structures like dams. This paper aims to assess the geomorphology characteristics such as topography of the embankment dam located at Bukit Kwong Dam, Kelantan Malaysia by utilizing the photogrammetric immages acquired from UAV including Orthomosaic, Digital Surface Model (DSM) and points clouds using Structure from Motion (SfM) approach. An accuracy assessment for the generated DSM containing topography information was made to prove the reliability of the data. As a result, the millimetre uncertainty in the form of Root Mean Square Error (RMSE) was calculated approximately 8.04 mm and 0.33 mm for both horizontal and vertical accuracy respectively. The results demonstrate a high level of reliability to ensure the accuracy of future works such as hazards prediction in the dam area.

show abstract

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Komu,

Nefeslioglu,

Gokceoglu

2024

IJGI

View full text Add to dashboard Cite

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers.

show abstract

Assessing the impact of RCP4.5 and RCP8.5 scenarios on landslide susceptibility mapping using support vector machine: A case study of Penang Island, Malaysia

Cited by 3 publications

References 80 publications

Social and Economic Impact in the Landslide Prone Zones and Related Policies

Social and Economic Impact in the Landslide Prone Zones and Related Policies

Mapping Embankment Dam Geomorphology Using Unmanned Aerial Vehicles (UAVs): A Case Study of Bukit Kwong Dam, Kelantan, Malaysia

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Contact Info

Product

Resources

About