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Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation

Journal of Degraded and Mining Lands Management · Vol. 10 No. 3 (2023) · hlm. 4479–4494

DOI
10.15243/jdmlm.2023.103.4479
ISSN
2502-2458, 2339-076X
Terbit
2023-04-01
Penerbit
Brawijaya University
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.

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  • University of Brawijaya
  • University of Göttingen
  • Universitas Islam Negeri Sultan Maulana Hasanuddin Banten
  • State University of Semarang
  • State University of Malang
  • Institut Pertanian Malang
  • Universitas Negeri Padang
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Rekan penulisOrganisasiArtikel bersama
Sri Rahayu UtamiUniversity of Brawijaya6
Endang ArisoesilaningsihUniversity of Brawijaya6
Sugeng PrijonoUniversity of Brawijaya4
Retno SuntariUniversity of Brawijaya4
Christanti AgustinaUniversity of Brawijaya3
Zaenal KusumaUniversity of Brawijaya3
Adji Achmad Rinaldo FernandesUniversity of Brawijaya3
Herawati HerawatiUniversity of Brawijaya2
Rodiyati AzrianingsihUniversity of Brawijaya2
Bagyo YanuwiadiUniversity of Brawijaya2
Anisa ZairinaInstitut Pertanian Malang2
Nia KurniawanUniversity of Brawijaya2
Marife D. CorreUniversity of Göttingen2
Mochtar Lutfi RayesUniversity of Brawijaya2
Zulfaidah Penata GamaUniversity of Brawijaya2
S SudartoUniversity of Brawijaya2
Muhammad Fadhil AnshariUniversity of Brawijaya2
Riska Putri QurotaayuinaUniversity of Brawijaya1
Retno UtamiState University of Semarang1
Iyan RobiansyahIndonesian Institute of Sciences1
Yatha Maziyyatul HijriyahUniversity of Brawijaya1
Eni NuraeniUniversitas Islam Negeri Sultan Maulana Hasanuddin Banten1
Dwi Hilda PutriUniversitas Negeri Padang1
Uswatun HasanahUniversitas Islam Negeri Sultan Maulana Hasanuddin Banten1
Muhammad AbdullahState University of Semarang1
Dwi Kameluh AgustinaUniversity of Brawijaya1
Akhmad RizaliUniversity of Brawijaya1
Saiful Arif AbdullahNational University of Malaysia1
Nabila Fitri RosyidaAfiliasi tidak diketahui1
Irfan MustafaUniversity of Brawijaya1
Wildania Putri AgustinaUniversity of Brawijaya1
Marsa SalsabilaUniversity of Brawijaya1
Riska Putri QurotaayuninaUniversity of Brawijaya1
Hakan BozdoğanAhi Evran University1
Edzo VeldkampUniversity of Göttingen1
Nur AzizahAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jdmlm.ub.ac.id/index.php/jdmlm/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Developing landslide susceptibility map using Artificial Neural Network (ANN) method for mitigation of land degradation</dc:title>
	<dc:creator>Masruroh, Heni</dc:creator>
	<dc:creator>Leksono, Amin Setyo</dc:creator>
	<dc:creator>Kurniawan, Syahrul</dc:creator>
	<dc:creator>Soemarno, Soemarno</dc:creator>
	<dc:subject xml:lang="en-US">artificial neural network</dc:subject>
	<dc:subject xml:lang="en-US">landslide susceptibility</dc:subject>
	<dc:subject xml:lang="en-US">remote sensing data</dc:subject>
	<dc:subject xml:lang="en-US">RStudio</dc:subject>
	<dc:description xml:lang="en-US">Landslides are one of the crucial problems that have an impact on land degradation and human life. This study aimed to develop vulnerability maps using ANN to mitigate land degradation in the Bromo Tengger Semeru with the extending area of Universal Transverse Mercator (UTM) Coordinate System Top 91277639, Bottom 911569, Left 692860, and Right 706860. The method applied the Artificial Neural Network (ANN) model using RStudio machine learning. Landslides were mapped using Sentinel Image and Orthomozaic photo interpretation from data acquisition using Unmanned Aerial Vehicle (UAV). The landslide control factor data was obtained through DEMNAS (National Digital Elevation Model) with a spatial resolution of 8 meters. Data normalisation was conducted using the Mix-Max method before it was processed using RStudio. The landslide existing for ANN workflow was processed using the Bioclim model. The results showed landslide susceptibility was categorised into four classes i.e., low susceptibility (29.83%), which was spatially spread on most in the lower slopes, moderate susceptibility (3.11%), high susceptibility (2.99%), and very high susceptibility (15.94) which is scattered on the upper slope to the middle slope of the watershed. The most significant factor influencing the landslide is the topography factor, with a Relative Importance (RI) value of 0.86; the hydrological factor, with an RI of 0.833 and the surface feature, with an RI of 0.355. The results of the landslide susceptibility model are very proper for land degradation mitigation strategies. It has high accuracy through an Area Under Curve (AUC) of 0.965 and a Precision Recall Curve (PRC) of 0.976.</dc:description>
	<dc:publisher xml:lang="en-US">Brawijaya University</dc:publisher>
	<dc:date>2023-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/1513</dc:identifier>
	<dc:identifier>10.15243/jdmlm.2023.103.4479</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Degraded and Mining Lands Management; Vol. 10 No. 3 (2023); 4479-4494</dc:source>
	<dc:source>2502-2458</dc:source>
	<dc:source>2339-076X</dc:source>
	<dc:source>10.15243/jdmlm.2023.103</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jdmlm.ub.ac.id/index.php/jdmlm/article/view/1513/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of Degraded and Mining Lands Management</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>