Landslide Susceptibility Mapping of Menoreh Mountain Using Logistic Regression

Nadia Sekarlangit; Teuku Faisal Fathani; Wahyu Wilopo

doi:10.22146/jag.72067

Landslide Susceptibility Mapping of Menoreh Mountain Using Logistic Regression

https://doi.org/10.22146/jag.72067

Nadia Sekarlangit^(1*), Teuku Faisal Fathani⁽²⁾, Wahyu Wilopo⁽³⁾

(1) Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada
(2) Center for Disaster Mitigation and Technological Innovation (GAMA-InaTek) Universitas Gadjah Mada
(3) Department of Geological Engineering, Faculty of Engineering, Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Menoreh mountain is one of the priority areas developed for tourism and to support sustainable development, it must pay attention to disaster aspects, one of which is landslides. The map published by Center for Volcanology and Geological Hazard Mitigation of Indonesia (PVMBG) has a regional scale, so it is necessary to have a more detailed landslide susceptibility map in the Menoreh Mountains. Identification and evaluation of the landslide conditioning factor were done using logistic regression so that the zonation of the probability of landslide susceptibility can be made. The data was used from field observation conducted at 372 locations including 129 locations where landslides occurred and from a local disaster management agency (BPBD) of 200 landslide locations. Significant landslide conditioning factors include slope, lithology, distance to lineaments, distance to river, and distance to road. The research area is divided into three susceptibility zones classified into low landslide susceptibility zone (0-0.33) covering 39.82%, moderate landslide susceptibility zone (0.34-0.66) covering 25.86%, and high landslide susceptibility zone (0.67-1.00) covering 34.31% of the whole area. Analysis using the logistic regression method has a model prediction accuracy rate of 90.5%, which means that it can predict landslide occurrence in the Menoreh Mountains accurately.

Keywords

Landslide conditioning factors; landslide prediction; landslide assessment; GIS

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DOI: https://doi.org/10.22146/jag.72067