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

Geoplanning: Journal of Geomatics and Planning · Vol. 11 No. 1 (2024) · hlm. 43–56

DOI
10.14710/geoplanning.11.1.43-56
ISSN
2355-6544
Terbit
2024-03-08
Penerbit
Department of Urban and Regional Planning, Diponegoro University
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc-sa/4.0

Abstrak

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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  • University of Bengkulu
  • Meteorological, Climatological, And Geophysical Agency
  • Indonesian Institute of Sciences
  • Earth Observatory of Singapore
  • Lampung University
  • State University of Jakarta
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Suhendra SuhendraUniversity of Bengkulu2
Halauddin HalauddinUniversity of Bengkulu2
Jesika Erni Elfrita SinagaUniversity of Bengkulu2
Refrizon RefrizonUniversity of Bengkulu2
Ulfa NuramadaniUniversity of Bengkulu1
Ardika Pratama PanjaitanUniversity of Bengkulu1
Ainul LisaUniversity of Bengkulu1
Siti Hanifah MaulaniUniversity of Bengkulu1
Lori Agung SatriaMeteorological, Climatological, And Geophysical Agency1
Suaidi AhadiMeteorological, Climatological, And Geophysical Agency1
B.N. ZegaUniversity of Bengkulu1
Shaoneng HeEarth Observatory of Singapore1
Rio SaputraUniversity of Bengkulu1
Edi KusmantoIndonesian Institute of Sciences1
Yosi Apriani PutriUniversity of Bengkulu1
Juhendi SinagaUniversity of Bengkulu1
Muhammad HasanudinIndonesian Institute of Sciences1
Lizalidiawati LizalidiawatiUniversity of Bengkulu1
Beni Syakban IdrisUniversity of Bengkulu1
RefrizonUniversity of Bengkulu1
Budi HarliantoUniversity of Bengkulu1
Gita Panny NababanUniversity of Bengkulu1
Erlan SumanjayaLampung University1
Muhammad Arib AlwansyahState University of Jakarta1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ejournal.undip.ac.id/index.php/geoplanning/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province</dc:title>
	<dc:creator>Abrar, Camelia Batun</dc:creator>
	<dc:creator>Lubis, Ashar Muda</dc:creator>
	<dc:creator>Fadli, Darmawan Ikhlas</dc:creator>
	<dc:creator>Akbar, Arya J</dc:creator>
	<dc:creator>Samdara, Rida</dc:creator>
	<dc:subject xml:lang="en-US">Landslide, Machine Learning, Frequency Ratio</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Urban and Regional Planning, Diponegoro University</dc:publisher>
	<dc:date>2024-03-08</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejournal.undip.ac.id/index.php/geoplanning/article/view/53381</dc:identifier>
	<dc:identifier>10.14710/geoplanning.11.1.43-56</dc:identifier>
	<dc:source xml:lang="en-US">Geoplanning: Journal of Geomatics and Planning; Vol 11, No 1 (2024); 43-56</dc:source>
	<dc:source>2355-6544</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejournal.undip.ac.id/index.php/geoplanning/article/view/53381/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 GJGP-UNDIP</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
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