Analysis of Landslide Susceptibility using Quantitative Multivariate Prediction Models in Jenelata Sub-Watershed, Jeneberang Watershed South Sulawesi Indonesia

Abstract

Landslides have taken an essential role in the evolution of landscapes, are present almost everywhere, and have become one of the catastrophic subjects of frequent natural disasters. The landslide disaster in Indonesia became one of the disasters that resulted in many casualties. Quantitative multivariate prediction models are statistical methods that allow us to research more than two variables simultaneously. Multivariate analysis is used because, in reality, the problem cannot be solved by simply connecting two variables or looking at the influence of one variable on another. The Jenelata Sub-Watershed of the Jeneberang Watershed is the upper reaches of the Jeneberang watershed, with areas that have undergone many changes in land use and experienced a very high erosion rate. It can be seen by the river flow, which always carries soil sedimentation. The 650 landslide events detected in Google Earth imagery serve as the basis for assessing the extent of landslides. Discrimination of causal factors in frequency ratio (FR) indicates that slope factors, river density, and slope direction are the leading causes of landslides. The NGO was then built using a logistic regression (LR) model. The ROC curve in the model is more inclined towards the sensitivity axis with the success of the AUC and predictions of 0.809 model validation and 0.801 prediction validation, respectively.