3 Citations (Scopus)


According to the Indonesian Disaster Management Agency (BNPB), Indonesia's losses due to landslides were estimated around hundreds of billion rupiah in 2017. Making landslide as one of the catastrophes with the greatest risk of loss and leaving a couple regions prone to landslides in Indonesia, Pacitan region is one of them. Landslide delineation therefore represents a particularly beneficial application of evolving research trend in disaster reduction, especially for the vulnerable region. In the present times of open-access satellite data, cloud computing and machine-learning algorithms is frequently used for disaster prevention monitoring. By employing Google Earth Engine, this study focuses on the susceptibility of landslide occurrence using a random forest machine-learning framework applied to digital topographic data such as elevation, slope and aspect as the independent variables and landslide inventory data obtained from Ministry of Energy and Mineral Resources Republic of Indonesia as the dependent variable. This study data sets composed from 1000 random points in Pacitan region with 70:30 ratio for training and testing sample points. The model produced good result, with overall accuracy values of 0.94, kappa values of 0.79 and 0.80 for AUC value. This model also showed that elevation is the most important variable in the landslide susceptible area. The results of this study can be used to evaluate the potential future impacts of landslide and help to optimize the management of disaster reduction in the region of Pacitan.

Original languageEnglish
Article number012028
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 13 Apr 2021
Event5th Geomatics International Conference 2020, GeoICON 2020 - Virtual, Online, Indonesia
Duration: 26 Aug 2020 → …


  • Google Earth Engine
  • Landslide Susceptibility Map
  • Pacitan
  • Random Forest


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