The Aplication of Landslide Inventory Data Base of Indonesia (LIDIA) For Supporting Landslide Susceptibility Mapping in Cianjur Regency, West Java, Indonesia

  • Sumaryono Sumaryono
  • Yukni Arifianti
  • Yunara Dasa Triana
  • Widya Ika
  • Wawan Irawan
  • Gede Suantika

Abstract

Susceptibility analysis for predicting landslides most frequently has been done using deterministic methods and statistical methods. Some landslide events caused loss of life, infrastructure and properties. In order to minimize the risk, the mitigation has been done. These damages can be mitigated if the cause and effect relationships of the events are knowing. One of the mitigation method is using landslide susceptibility map. In this study, we used Weight of Evidence methods to produce landslide susceptibility map. The study had been carried out, using Landslide Inventory Database of Indonesia (LIDIA), remote sensing data, field surveys and geographic information system (GIS) tools. This method can be used without requiring geotechnical, groundwater or failure depth data. However, the other factors to influence landslide occurrence, such as elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, soil texture, precipitation, land use/land cover (LULC) and NDVI were considered. Then analytical result verified by using test data of landslide, and the AUC success rate is 0.85 and AUC prediction rate is 0.79 with difference 0.06. This conditions the allowed tolerance of 15%. This has shown good model of landslide susceptibility. The obtained landslide susceptibility map and landslide inventory data base of Indonesia can be used for landslide hazard prevention and mitigation, and proper planning for land use in the future.

Published
2018-05-16
How to Cite
SUMARYONO, Sumaryono et al. The Aplication of Landslide Inventory Data Base of Indonesia (LIDIA) For Supporting Landslide Susceptibility Mapping in Cianjur Regency, West Java, Indonesia. GSTF Journal of Geological Sciences (JGS), [S.l.], v. 1, n. 2, may 2018. ISSN 2335-6782. Available at: <http://dl6.globalstf.org/index.php/jgs/article/view/1617>. Date accessed: 16 june 2019.