Geomatics and Environmental Engineering, vol. 18, no. 2

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Vol. 18 No. 2 (2024): Geomatics and Environmental Engineering

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Submitted
Dec 8, 2022
Accepted
Feb 6, 2024
Published
Mar 16, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia

https://doi.org/10.7494/geom.2024.18.2.73
Hana Listi Fitriana
Department of Water Resources Management, West Java Province, Indonesia
https://orcid.org/0000-0001-6752-8414
Rido Dwi Ismanto
National Research and Innovation Agency (BRIN), Research Center for Computing, Cibinong, Indonesia
https://orcid.org/0000-0002-2903-6731
Jessica Stephanie Tulus
University of Indonesia, Department of Geophysics, Depok, Indonesia
https://orcid.org/0000-0002-0619-1667
Atriyon Julzarika
National Research and Innovation Agency (BRIN), Research Center for Limnology and Water Resources, Cibinong, Indonesia
https://orcid.org/0000-0002-4531-5327
Jalu Tejo Nugroho
National Research and Innovation Agency (BRIN), Research Center for Geoinformatics, Jakarta, Indonesia
https://orcid.org/0000-0002-8615-2640
Johannes Manalu
National Research and Innovation Agency (BRIN), Research Center for Geoinformatics, Jakarta, Indonesia
https://orcid.org/0009-0000-1761-3725

Corresponding Author(s) : Hana Listi Fitriana

hanalisti@yahoo.com

Geomatics and Environmental Engineering, Vol. 18 No. 2 (2024): Geomatics and Environmental Engineering

Abstract

Landslides have produced several recurrent dangers, including losses of life and property, losses of agricultural land, erosion, population relocation, and others. Landslide mitigation is critical since population and economic expansion are rapidly followed by significant infrastructure development, increasing the risk of catastrophes. At an early stage in landslide-disaster mitigation, landslide-risk mapping must give critical information to help policies limit the potential for landslide damage. This study will utilize the comparative frequency ratio (FR) and random forest (RF) techniques; they will be utilized to properly investigate the distribution of flood vulnerability in the Sumedang area. This study has identified 12 criteria for developing a landslide-susceptibility model in the research region based on the features of past disasters in the research area. The FR and RF models scored 88 and 81% of the AUC value, respectively. Based on the McNemar test, the FR and RF models featured the same performance in determining the landslide-vulnerability level performances in Sumedang. They performed well in assessing landslides in the research region; therefore, they may be used as references in landslide prevention and references in future regional development plans by the stakeholders.

Keywords

landslide susceptibility analysis frequency ratio random forest Sumedang
Fitriana, H. L., Ismanto, R. D., Tulus, J. S., Julzarika, A., Nugroho, J. T., & Manalu, J. (2024). Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia. Geomatics and Environmental Engineering, 18(2), 73–95. https://doi.org/10.7494/geom.2024.18.2.73
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