Analysis of Landslide Susceptibility Zone using Frequency Ratio and Logistic Regression Method in Hambalang, Citeureup District, Bogor Regency, West Java Province

Abstract

Hambalang area is one of the regions susceptible to landslide events. This is due to unstable geological conditions and high rainfall. Administratively, research area included in Citeureup District, Bogor Regency, West Java Province. Astronomically, research area is located at the coordinates 106°51’30” - 106°53’30” East Longitude and 06°32’ - 06°34’ South Latitude. This study aims to determine the geological conditions of the study area and conduct susceptibility zoning in the Hambalang area using the method of Frequency Ratio and Logistic Regression. The geological conditions of the study area consist of geomorphology in the form of corrugated and flatland morphological units, stratigraphy consisting of claystone units, andesite units and alluvial units, and the structure of the Sumurbatu anticline. The parameters used to analyze the causes of landslide are slope, geology, rainfall, soil type, land use and distance from rivers. There were 19 landslide events identified in the study area. The relationship between occurrences of landslide and the parameters mentioned earlier can be quantified using the value of Frequency Ratio and Logistic Regression. Based on the results of the validation with the Frequency Ratio method, the AUC value of 0.6854 which shows the model of landslide susceptibility based on the selection of parameters and the adequacy of the landslide event data is good. The value of the frequency ratio of the model is divided into three zones of susceptibility, namely high (42.30%), moderate (20.34%) and low (37.36%). Based on the results of the validation with the Logistic Regression method, the AUC value of 0.762 also shows the landslide susceptibility model based on the selection of parameters and the adequacy of the landslide event data so that the Logistic Regression value of the model is divided into three susceptibility zones, namely high (56.89%), moderate (19.53%) and low (23.68%).

Analisis Struktur Bawah Permukaan Tanah Menggunakan Metode Geolistrik 3D Studi Kasus: Area Kampus Universitas Pertahanan Republik Indonesia, IPSC, Bogor

“…Tindakan yang salah akan mempengaruhi kondisi dari infrastruktur yang dibangun diatasnya. Karena parameter yang mempengaruhi terdiri dari dua parameter yaitu litologi dan penggunaan lahan (Hidayat, et al, 2019). Oleh karena itu, perlu diketahui informasi struktur bawah permukaan.…”

Landslide Early Warning Systems: Requirements and Solutions for Disaster Risk Reduction—India

“…However, there has been an increasing global interest in designing, developing, and deploying landslide early warning systems as a solution to disaster risk reduction (Guzzetti et al 2020;Pecoraro et al 2019). New geographical areas are being explored for the deployment of landslide early warning systems utilizing the application of geospatial technology and Web-GIS in order to save human lives by utilizing precipitation measurement as a key indicator for a regional level warning (Ahmed et al 2020;Hidayat et al 2019).…”

Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershed, Ethiopia

“…Landslide susceptibility models based on the bivariate frequency and weights of evidence models [34] and FR and information value (IV) models [1,10,35], machine learning models [36,37], and deep learning models [38,39] have been developed. With the development of Geographic Information System (GIS), other researchers have used bivariate FR and multivariate logistic regression models [40][41][42][43][44] to help in the calculation and visualization of the cumulative effects of conditioning factors on landslides.…”