DOI: https://doi.org/10.14710/geoplanning.11.1.43-56

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

Camelia Batun Abrar  -  Jurusan Fisika, UNIB, Indonesia
*Ashar Muda Lubis  -  Jurusan Fisika, FMIPA, Universitas Bengkulu, Indonesia
Darmawan Ikhlas Fadli  -  Jurusan Fisika, UNIB, Indonesia
Arya J Akbar  -  , Indonesia
Rida Samdara  -  Jurusan Fisika, UNIB, Indonesia

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Abstract

Landslides occur when masses of rock, debris or soil move due to various factors and processes that cause land movement. The Taba Penanjung-Kepahiang route is one of the areas in Bengkulu Province that is highly prone to landslides. This causeway is the only fastest land route connecting the Bengkulu-Kepahiang area. In recent years, the road area has often been cut off due to landslides and fallen trees, which have caused road access to be cut off and obstructed and claimed lives. This study uses a Machine Learning (ML) and GIS approach with Variable Frequency Ratio using 16 independent factors obtained from the spatial database and DEM, which correlate with landslide events. This research aims to gain an in-depth understanding of the factors that cause landslides. In addition, the research focus is the development of a Disaster Mitigation Model to design and implement effective strategies to reduce the risk and impact of landslide disasters through in-depth analysis The dependent factor is the location of the landslide from the historical landslide area for the last five years, with a distribution of 70/30%. Furthermore, frequency ratio is used to analyze the correlation between conditioning factors and historical landslides. Then, the independent and dependent factors were normalized to create a landslide susceptibility map. Frequency Ratio (FR) indicates the likelihood of an event occurring, with drainage density (FR= 0.69), shear wave velocity (Vs30) (FR= 0.66), slope (FR= 0.60), and rainfall (FR= 0.55).  The output of the processed data is in the table below.

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Keywords: Landslide, Machine Learning, Frequency Ratio
Funding: Department of Physics, University of bengkulu

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Section: Original Research
Language : EN
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