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

• Autorzy: Hana Listi Fitriana , Jessica Stephanie Tulus , Jalu Tejo Nugroho , Rido Dwi Ismanto , Atriyon Julzarika , Johannes Manalu
• Języki publikacji: EN

Abstrakty

EN

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.(original abstract)

Słowa kluczowe

EN

Landslide; Regional development

PL

Osuwisko; Rozwój regionalny

• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2024
• Numer: nr 18/2
• Strony: 73--95

Twórcy

autor

Hana Listi Fitriana

• Department of Water Resources Management, West Java Province, Indonesia

autor

Jessica Stephanie Tulus

• University of Indonesia, Department of Geophysics, Depok, Indonesia

autor

Jalu Tejo Nugroho

• National Research and Innovation Agency (BRIN), Research Center for Geoinformatics, Jakarta, Indonesia

autor

Rido Dwi Ismanto

• National Research and Innovation Agency (BRIN), Research Center for Computing, Cibinong, Indonesia

autor

Atriyon Julzarika

• National Research and Innovation Agency (BRIN), Research Center for Limnology and Water Resources, Cibinong, Indonesia

autor

Johannes Manalu

• ational Research and Innovation Agency (BRIN), Research Center for Geoinformatics, Jakarta, Indonesia

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Identyfikatory

DOI

10.7494/geom.2024.18.2.73