The Use of Machine Learning for Accessing Landslide Susceptibility Class: Study Case of Kecamatan Pacet, Kabupaten Mojokerto

Abstract

Kecamatan Pacet, Kabupaten Mojokerto is one of an area with many landslide events in East Java Province. As a mitigation effort, this research aimed to map the landslide susceptibility class distribution of the research area. This research applied a machine learning analysis technic which combined Frequency Ratio (FR) and Logistic Regression (LR) models to assess the landslide susceptibility class distribution. FR bivariate analysis is used to normalized the data and to identify the influence significancy on each class of triggering factors. LR multivariate analysis is applied to generate the landslide probability (susceptibility) and to show the influence significancy of each triggering factor to landslide events. There are 12 triggering factors to landslide used in this research, which is: TPI, TWI, SPI, slope, aspect, elevation, profile curvature, distance to drainage, geological unit, rainfall, land use, and distance to the road. This research has 383 landslides and 383 non-landslide events as the data sample based on field survey, BPBD Kabupaten Mojokerto, and Google Earth Pro imagery interpretation. The proportion of dataset training and testing is 70% and 30%, which generated from the data inventory. This research used ROC analysis to validate the landslide susceptibility model. The result showed that the landslide susceptibility model has an AUC value of 0.91, which indicated that the model has high accuracy.

Artificial Intelligence-Based Landslide Studies in Indonesia: A Systematic Review in Recent Years

“…The other popular AI method that is commonly used for building LSM is Logistic Regression (LR). At least four papers use this multivariate statistical approach, where several researchers combine LR with another AI method, like ANN, DT, or Frequency Ratio (FR), to generate LSM [6], [8], [11], [14]. LSM produced by LR has an accuracy range of 68.7-85% [8], [11].…”

“…On the other hand, researchers often contrast the performance of the AI method in generating landslide studies with various types of Digital Elevation Models (DEM). The selection of the DEM sources involves diverse considerations such as availability, resolution, compatibility, etc [7], [13], [14]. These diversified using DEM sources also produce different modelling results that are also interesting to discuss.…”

Mapping Landslide Vulnerability using Machine Learning Approach along the Taba Penanjung-Kepahiang Road, Bengkulu Province

“…In this research we have been successfully implementing the ML approach to access and to map landslide vulnerability along Taba Penanjung Kepahiang road. Previously The ML has been widely used in landslide mapping in several regions in Indonesia, such as Aldiansyah & Wardani (2024), Darminto et al (2021) and Irawan et al, (2021). Previous researches used ML with different algorithms, with almost the same input parameters.…”

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

“…TPI parameter shows the difference in elevation to analyze the valley, slope, mountain ridge, and ridge sections (Bachri et al, 2019;Irawan, Sumarmi, Bachri, Panoto, Nabila, et al, 2021). The TPI calculation results (Figure 2) at the lowest number indicate valley topography, and the highest value indicates a ridge.…”