Analysis Geological and Geophysical Data for Prediction Landslide Hazard Zone with Weight of Evidence Method in Pacitan District East Java

  • Radhitya Adzan Hidayah Institut Sains & Teknologi AKPRIND Yogyakarta, Jalan Kalisahak No 28, Yogyakarta
  • Nurul Dzakiya Institut Sains & Teknologi AKPRIND Yogyakarta, Jalan Kalisahak No 28, Yogyakarta
Keywords: Pacitan, GIS, Weight of Evidence, Landslide

Abstract

Pacitan district have an interesting anomaly. Every time mostly impacted by disaster especially landslide. Landslides in their various forms are common hazard in mountainous terrain, especially in seismically active areas and regions of high rainfall. Landslides are one of the most common natural hazards in the Southern Range East Java terrain, causing widespread damage to property and infrastructure, besides the loss of human lives almost every year. The aim of this study predicted the potential landslide using Weight of Evidence Method. The geological data used lithological data, structural data, contour data and, alteration. Results from this data analysis are six evidence maps, such as NE-SW lineament, NW-SE lineament, host rock, heat source, kaolinite alteration and iron oxide alteration maps. The geophysical data analysis the distribution of rock density to interpretation the landslides. Evidence maps were analyzed by weight of evidence methods to result in favorable maps where the validity was tested using conditional independence (CI), the pairwise and overall tests. Then, the analyses produced a posterior probability map of the landslide. Posterior probability map (mineral potential maps) was validated by checking field. Posterior probability map (after validation) or favorable map predicted approximately favorable zone and non-favourable zones. Favorable zones of Potential Landslide Hazard Zonation, are divided into three classes. They are high-potensial hazard, moderate hazard and low hazard.

 

Keywords: Pacitan, GIS, Weight of Evidence, Landslide

References

Bonham-Carter (1994) Geographic Information System for Geoscientists: Modelling with GIS: Pergamon Press, Oxford, 398p.

Dai, F.C., Lee, C.F (2002) Landslide risk assessment and management: an overview. Engineering Geology, 64: 65-87.

G Gullà, L Antronico, P Iaquinta, O Terranova Geomorphology (2008). Elsevier, Vol 87, 250-267.

Hidayah, R.A. (2015) GIS application and mineral potential mapping of cooper and gold in Pacitan District, East Java Province, Thesis. Gadjah Mada Universty.

Maquaire (2002) Aléas géomorphologiques (mouvements de terrain) : Processus, fonctionnement, cartographie.Mémoire d'habilitation à Diriger des Recherches : Université Louis Pasteur, Strasbourg, 219 p. + 1 volume d'annexes.

ML Süzen, V Doyuran (2004) Data Driven Bivariate Landslide Susceptibility Assessment Using Geographical Information Systems: A Method and Application To Asarsuyu Catchment, Turkey, Engineering Geology Elsevier, Vol 71 303-321.

Taki, H. M., & Lubis, M. Z. (2017). Modeling accessibility of community facilities using GIS: case study of Depok City, Indonesia. Journal of Applied Geospatial Information, 1(2), 36-43.

Westen, C.J., Van Asch, T.W.J., Soeters, R.,( 2006) : Landslide hazard and risk zonation: why is it still sodifficult: Bulletin of Engineering Geology and the Environment, 65, 167–184.

Westen, Van C.J., (1993) : Application of Geographic Information Systems to Landslide Hazard Zonation. Ph-D Dissertation, Technical University Delft: ITC-Publication Number 15, ITC, Enschede, The Netherlands, 245 p.

Yalcin, A., 2008. GIS-Based Landslide Susceptibility Mapping Using Analytical Hierarchy Process and Bivariate Statistics In Ardesen (Turkey): Comparisons of results and confirmations. CATENA, 72(1): 1-12.

Yannick Thiery, 2006, Test of Fuzzy Logic Rules for Landslide Susceptibility Assessment, SAGEO (2006) Colloque International de Géomatique et d'Analyse Spatiale: Recherches & Développements. Strasbourg, 16 p.
Published
2018-09-17