Quantitative Hazard Analysis for Landslides in Hulu Kelang area, Malaysia

Authors

  • Nader Saadatkhah Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor Malaysia
  • Azman Kassim Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor Malaysia
  • Lee Min Lee Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 53300 Kuala Lumpur, Malaysia
  • Gambo Haruna Yunusa Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor Malaysia

DOI:

https://doi.org/10.11113/jt.v73.2977

Keywords:

Landslide susceptibility, probability-frequency ratio, bivariate approach

Abstract

Hulu Kelang is a region in Malaysia which is very susceptible to landslides. From 1990 to 2011, a total of 28 major landslide events had been reported in this area. This paper evaluates and compares the probability-frequency ratio (FR), statistical index (Wi), and weighting factor (Wf), used for assessing landslide susceptibility in the study area. Eleven landslide influencing factors were considered in the analyses. These factors included lithology, land cover, curvature, slope inclination, slope aspect, drainage density, elevation, distance to lake and stream, distance to road and trenches and two indices (the stream power index (SPI) and the topographic wetness index (TWI)) found in the area. The accuracy of the maps produced from the three models was verified using a receiver operating characteristics (ROC). The verification results indicated that the probability-frequency ratio (FR) model which was developed quantitatively based on probabilistic analysis of spatial distribution of historical landslide events was capable of producing a more reliable landslide susceptibility map in this study area compared to its other counterparts. About 89% of the landslide locations have been predicted accurately by using the FR map. 

References

Akgün, A, and F. Bulut. 2007. GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology 51: 1348–1377

Ali, F. 2000. Unsaturated Tropical Residual Soils and Rainfall Induced Slopes in Malaysia. Asian Conference on Unsaturated Soils Singapore. 41(52): 18–19

Carrara, A. 1983. A multivariate model for landslide hazard evaluation. Mathematical Geology 15: 403–426

Cevik, E, and T. Topal. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology. 44: 949–962

Chung, C.J.F, and A.G. Fabbri. 2003. Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30: 451–472

Conoscenti, C., D.M. Ciprioano, and E. Rotigliano. 2008. GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94: 325–339

Dai, F.C., C.F. Lee, J. Li, and Z.W. Xu. 2001. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology. 43: 381–391

Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, T. Masuda, and K. Nishino. 2008. GIS based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology. 54: 311–324

Dongyeob, K., I. Sangjun, H.L. Sang, H. Youngjoo, and C.H. Kyung-Sub. 2010. Predicting the rainfall-triggered landslides in a forested mountain region using TRIGRS model. Journal of Mountain Science. 7: 83–91

Dwikorita, K., F.F. Teuku, I. Sudarno, A. Budi, L. Djoko, and W. B. Paul. 2011. Landslide hazard and community-based risk reduction effort in Karanganyar and the surrounding area, central Java, Indonesia. Journal of Mountain Science. 8: 149–153

Ercanoglu, M., and C. Gokceoglu. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 41: 720–730

Ercanoglu, M., C. Gokceoglu, and T.H.W.J. Van-Asch. 2004. Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards 32: 1–23

Galli, M., F. Ardizzone, M. Cardinali, F. Guzzetti, and P. Reichenbach. 2008. Comparing landslide inventory maps. Geomorphology 94: 268–289

García-Rodríguez, M.J., J.A. Malpica, B. Benito, and M. Díaz. 2008. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology. 95: 172–191

Gruber, S.,and S. Peckham. 2008. Land-surface parameters and objects specific to hydrology. In Hengl T, Reuter HI, (eds.), Geomorphometry: Concepts, Software and Applications. Developments in Soil Science. 33:127–142

Guzzetti, F., P. Reichenbach, M. Cardinali, M. Galli, and F. Ardizzone. 2005. Landslide hazard assessment in the Staffora basin, northern Italian Apennines. Geomorphology. 72: 272–299

Guzzetti, F., P. Reichenbach, F. Ardizzone, M. Cardinali, and M. Galli. 2006. Estimating the quality of landslide susceptibility models. Geomorphology. 81: 166–184

Kanungo, D.P., M.K. Arora, S. Sarkar, and R.P. Gupta. 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology. 85: 347–366

Lee, M.L., Y.N. Kim, F.H. Yuk, and C.L. Wei. 2013. Rainfall-induced landslides in Hulu Kelang area, Malaysia. Nat Hazards.

Lee, S, and K. Min. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology. 40: 1095–1113

Lee, S. 2005. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing. 26: 1477–1491

Lee, S, and B. Pradhan. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4: 33–41

Lee, L.M., N. Gofar, and H. Rahardjo. 2009. A simple model for preliminary evaluation of rainfall-induced slope instability. Geology Engineering. 108(3–4): 272–285

Mohammadi, M. 2008. Mass movement hazard analysis and presentation of suitable regional model using GIS (Case Study: A part of Haraz Watershed). M.Sc. Thesis. Tarbiat Modarres University International Campus, Iran

Montgomery, D.R., W.E. Dietrich, R. Torres, S. P. Anderson, J.T. Heffner, and K. Loague. 1997. Hydrologic response of a steep, unchanneled valley to natural and applied rainfall. Water Recourse Research. 33: 91–109

Mora, C.S, and W.G. Vahrson. 1994. Macrozonation methodology for landslide hazard determination. Bulletin of the Association of Engineering Geologists. 31: 49–58.

Nandi, A, and A. Shakoor. 2009. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology. 110: 11–20

Nefeslioglu,, H.A., T.Y. Duman, and S. Durmaz. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Easten Black Sea Region of Turkey). Geomorphology. 94: 401–418

Nilsen, T.H., R.H. Wright, T.C. Vlasic, and W.E. Spangle. 1979. Relative slope stability and land-use planning in the San Francisco Bay Region, California, US. Geological Survey Professional Paper. 994: 96

Oztekin, B, and T. Topal. 2005. GIS-based detachment susceptibility analyses of a cut slope in limestone, Ankara—Turkey. Environmental Geology. 49: 124–132

Pourghasemi, H.R., B. Pradhan, C. Gokceoglu, M. Mohammadi, and H. R. Moradi. 2012. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences. 14: 1–15

Pradhan, B., R.P. Singh, and M. F. Buchroithner, 2006. Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Advances in Space Research. 37: 698–709

Pradhan, B, and S. Lee. 2010. Regional landslide susceptibility analysis using backpropagation neural network model at Cameron Highland, Malaysia. Landslides. 7: 13–30

Pradhan, B., S. Lee, and M.F. Buchroithner. 2010. Remote sensing and GIS based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogrammetrie Fernerkundung. Geoinformation. 1: 17–32

Pradhan, B. 2012. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences. 51: 350–365

Refice, A, and D. Capolongo. 2002. Probabilistic modeling of uncertainties in earthquake induced landslide hazard assessment. Computer Geoscience. 28: 735–749

Rutter, A.J., K.A. Kershaw, P.C. Robins, and A.J. Morton. 1971. A predictive model of rainfall interception in forests I. Derivation of the model from observations in a plantation of Corsican pine. Agricultural and Forest Meteorology. 9: 367–394

Rutter, A.J., A.J. Morton, and P.C. Robins. 1975. A predictive model of rainfall interception in forests, II. Generalization of the model and comparison with observations in some coniferous and hardwood stands. Journal of Applied Ecology. 12: 367–380.

Saadatkhah, N., A. Kassim, and L.M. Lee. 2014. Hulu Kelang, Malaysia regional mapping of rainfall-induced landslides using TRIGRS model. Arabian Journal of Geosciences. 1–12.

Sarkar, S., D.P. Kanungo, A.K. Patra, and P. Kumar. 2008. GIS based spatial data analysis for landslide susceptibility mapping. Journal of Mountain Science. 5: 52–62

Sarkar, S, and R. Anbalagan. 2008. Landslide hazard zonation mapping and comparative analysis of hazard zonation maps. Journal of Mountain Science. 5: 232–240

Sidle, R.C., Y. Tsuboyama, S. Noguchi, I. Hosoda, M. Fujieda, and T. Shimizu. 2000. Stream flow generation in steep headwaters: a linked hydrogeomorphic paradigm. Hydrological Process. 14: 369–385

Sidle, R.C, and M. Chigira. 2004. Landslides and debris flows strike Kyushu, Japan. Transactions American Geophysical Union 85(15): 145-151

Sidle, R.C, and H. Ochiai. 2006. Landslides: Processes, Prediction, and Land Use. Water Resource Monograph. 18: 312

Suzen, M.L, and V. Doyuran. 2004. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology. 71: 303–321

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science. 240: 1285–1293

Thiery, Y., J.P. Malet, S. Sterlacchini, A. Puissant, and O. Maquaire. 2007. Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology. 92: 38–59

Tsukamoto, Y., T. Ohta, and H. Noguchi. 1982. Hydrogeological And Geomorphological Studies Of Debris Slides On Forested Hillslopes In Japan. IAHS Publication. 137: 89–98

Uchida, T., K.I. Kosugi, and T. Mizuyama. 2002. Effects of pipe flow and bedrock groundwater on runoff generation in a steep headwater catchment in Ashiu, central Japan. Water Resources Research 38(111): 9–14

Van-Westen, C.J. 1997. Statistical Landslide Hazard Analysis, ILWIS 2.1 For Windows Application Guide. ITC Publication Enschede. 73–84

Wang, H.B, and K. Sassa. 2005. Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology. 47: 956–966

Wilson, J.P, and J.C. Gallant. 2000. Terrain Analysis Principles And Applications. Wiley, New York, 303

Yesilnacar, E, and T. Topal. 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology. 79: 251–266

Zhou, G., T. Esaki, Y. Mitani, M. Xie, and J. Mori. 2003. Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Engineering Geology. 68: 373–386

Downloads

Published

2015-02-09

Issue

Section

Science and Engineering

How to Cite

Quantitative Hazard Analysis for Landslides in Hulu Kelang area, Malaysia. (2015). Jurnal Teknologi (Sciences & Engineering), 73(1). https://doi.org/10.11113/jt.v73.2977