Mapping of landslide-prone areas in the Lisu river basin Barru Regency based on binary logistic regression


I G Sudarman, - and A Ahmad, - (2021) Mapping of landslide-prone areas in the Lisu river basin Barru Regency based on binary logistic regression. IOP Publishing.

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Abstract (Abstrak)

Barru Regency Government once issued an emergency response status for floods and landslides on December 28, 2018. According to data from the Barru Regency Regional Disaster Management Agency, from 2013 to 2019, there were always landslides in the Districts of Tanete Riaja, Pujananting, Tanete Rilau, and Barru. This area is included in the Lisu Watershed. This will produce a map of the distribution of the landslide-prone regions in the Lisu watershed. This study uses Binary Logistic Regression (BLR) and NDVI (Normalized difference vegetation index) analysis. The data used are landslide points, soil types, lithology, slopes, land use, rainfall, soil texture, and river distance. One hundred thirty-seven landslide points were recorded that were successfully obtained through field surveys and NDVI image analysis. The research area has a type B climate according to the Schmidt-Ferguson classification system. The variables of rainfall, land-use class, lithology type, and slope class significantly affect regression modeling with significance values of 0.042, 0.000, 0.003, and 0.000, respectively. Variables of rainfall, distance from the river, slope class, lithology type, and land use class significantly affect regression modeling. Landslides occur on slopes from rather steep to very steep slopes. A total of 60 landslides occurred in the Camba Formation. A total of 93 landslides occurred in the scrub land-use class. Landslide-prone areas with a slightly hazardous class covering an area of 4386 hectares, 4031 hectares of prone, and 4275 hectares of very prone areas are generally scattered in the Lisu River Basin's southern region.

Item Type: Article
Subjects: S Agriculture > S Agriculture (General)
Depositing User: - Andi Anna
Date Deposited: 28 Jun 2022 01:35
Last Modified: 28 Jun 2022 01:35
URI: http://repository.unhas.ac.id:443/id/eprint/17252

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