GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND FACTOR EFFECT ANALYSIS BY CERTAINTY FACTOR IN UPSTREAM OF JENEBERANG RIVER, INDONESIA

DOI: https://doi.org/10.14710/geoplanning.5.1.75-90
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Published: 25-04-2018
Section: Articles
Fulltext PDF Tell your colleagues Email the author

This study aimed to assess landslide susceptibility by employing certainty factors model (CF) to select the causative factors for landslide susceptibility mapping in Upstream of Jeneberang River, South Sulawesi. Indonesia. The landslide causative factors were: soil, slope angle, aspect, elevation, lithology, land use, distance to the river, drainage density, and precipitation. For validation purpose, landslide inventory map was randomly partition into two groups, 30% for the validation and 70% for the training. Landslide susceptibility maps were produced by logistic regression using original factor (all nine factors) and selected factor (four factors with positive CF value). The result of certainty factor analysis shows CF value is positive for elevation, land use, slope and drainage density. The accuracy of two landslide susceptibility maps were evaluated by calculating the area under the curve of Receiver Operating Characteristic (ROC) curves. The result shows the the success rate curve for nine factor map (80.2%)  is higher than four factor map (78%). But in case of closeness between success rate curve and predictive rate curve, certainty factors model has a closer distance. In this study, effect analysis studies show how the accuracy changes when the input factors are changed.

Keywords

Landslide; Susceptibility Map; GIS; Certainty Factor; Logistic Regression

  1. Putri Fatimah Nurdin 
    Kyushu University, Japan

    Graduate School of Bioresource and Bioenvironmental Science.

    Laboratory of Forest Conservation and Erosion Control.

    Kyushu University, Fukuoka - Japan

  2. Tetsuya Kubota 
    Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki Higashi-Ku Fukuoka 812-8581, Japan, Japan
  1. Ayalew, L., Yamagishi, H., Marui, H., & Kanno, T. (2005). Landslides in Sado Island of Japan: Part {II}. {GIS}-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, 81(4), 432–445. [Crossref]

  2. Brabb, E. E. (1985). Innovative approaches to landslide hazard and risk mapping. In International Landslide Symposium Proceedings, Toronto, Canada (Vol. 1, pp. 17–22). [Crossref]

  3. Chung, C.-J. F., & Fabbri, A. G. (2003). Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards, 30(3), 451–472. [Crossref]

  4. Chung, C.-J. F., & Fabbri, A. G. (2012). Systematic Procedures of Landslide Hazard Mapping for Risk Assessment Using Spatial Prediction Models. In Landslide Hazard and Risk (pp. 139–174). John Wiley & Sons, Ltd. [Crossref]

  5. Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., & Chacón, J. (2012). Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Natural Hazards and Earth System Sciences, 12(2), 327–340. [Crossef]

  6. CTI Engineering. (2006). Report on Urgent Survey for Consulting Engineering Services of Bawakaraeng Urgent Sediment Control Project, Ministry of Public Works, Indonesia.

  7. Cuesta, M. J. D., Sánchez, M. J., & Garci’a, A. R. (1999). Press archives as temporal records of landslides in the North of Spain: relationships between rainfall and instability slope events. Geomorphology, 30(1–2), 125–132. [Crossef]

  8. Ercanoglu, M., & Gokceoglu, C. (2004). Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75(3–4), 229–250. [Crossef]

  9. Glenn, N. F., Streutker, D. R., Chadwick, D. J., Thackray, G. D., & Dorsch, S. J. (2006). Analysis of {LiDAR}-derived topographic information for characterizing and differentiating landslide morphology and activity. Geomorphology, 73(1–2), 131–148. [Crossef]

  10. Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1–4), 181–216. [Crossef]

  11. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1–4), 272–299. [Crossef]

  12. Hasegawa, S., Yamanaka, M., Mimura, T., Dahal, R. K., & Nonomura, A. (2009). Drainage density as rainfallinduced landslides susceptibility index. In International Seminar on Hazard Management for Sustainable Development in Kathmandu, Nepal (pp. 72–75).

  13. HASNAWIR, & KUBOTA, T. (2012). Rainfall Threshold for Shallow Landslides in Kelara Watershed, Indonesia. International Journal of Erosion Control Engineering, 5(1), 86–92. [Crossef]

  14. Lee, C. F., Li, J., Xu, Z. W., & Dai, F. C. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381–391. [Crossef]

  15. Lee, S., Ryu, J.-H., Won, J.-S., & Park, H.-J. (2004). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71(3–4), 289–302. [Crossef]

  16. Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982–990. [Crossef]

  17. Magliulo, P., Lisio, A. Di, Russo, F., & Zelano, A. (2008). Geomorphology and landslide susceptibility assessment using {GIS} and bivariate statistics: a case study in southern Italy. Natural Hazards, 47(3), 411–435. [Crossef]

  18. McCullagh, P., & Nelder, J. A. (1989). An outline of generalized linear models. In Generalized Linear Models (pp. 21–47). Springer {US}. [Crossef]

  19. Meten, M., PrakashBhandary, N., & Yatabe, R. (2015). Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia. Geoenvironmental Disasters, 2(1). [Crossef]

  20. Moreiras, S. M. (2005). Landslide susceptibility zonation in the Rio Mendoza Valley, Argentina. Geomorphology, 66(1–4), 345–357. [Crossef]

  21. Ngadisih, Yatabe, R., Bhandary, N. P., & Dahal, R. K. (2013). Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province, Indonesia. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 8(1), 29–47. [Crossef]

  22. Shortliffe, E. H., & Buchanan, B. G. (1975). A model of inexact reasoning in medicine. Mathematical Biosciences, 23(3–4), 351–379. [Crossef]

  23. Tsuchiya, S., Koga, S., Sasahara, K., Matsui, M., Nakahiro, M., Watanabe, H., … Yoshida, K. (2004). Reconnaissance of the gigantic landslide occurred on Mt. Bawakaraeng in the south Sulawesi state of Indonesia and unstable debris sedimentation (prompt report). Journal of the Japan Society of Erosion Control Engineering, 57(3), 40–46.

  24. Tsuchiya, S., Sasahara, K., Shuin, S., & Ozono, S. (2009). The large-scale landslide on the flank of caldera in South Sulawesi, Indonesia. Landslides, 6(1), 83–88. [Crossef]

  25. Van Westen, C. J., Rengers, N., & Soeters, R. (2003). Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment. Natural Hazards, 30(3), 399–419. [Crossef]

  26. Walker, L. R., & Shiels, A. B. (2013). Large scales and future directions for landslide ecology. In Landslide Ecology (pp. 227–240). Cambridge University Press. [Crossref]