Sains Malaysiana 48(7)(2019): 1367–1381

http://dx.doi.org/10.17576/jsm-2019-4807-06

Aplikasi Sistem Maklumat Geografi (GIS) dan Analisis Diskriminan dalam Pemodelan Kejadian Kegagalan Cerun di Pulau Pinang, Malaysia

(Application of Geographical Information Systems (GIS) and Discriminant Analysis in Modelling Slope Failure Incidence in Pulau Pinang, Malaysia)

NURIAH ABD MAJID¹* & RUSLAN RAINIS²

¹Institut Alam Sekitar dan Pembangunan (LESTARI), Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia

²Bahagian Geografi, Pusat Pengajian Ilmu Kemanusiaan, Universiti Sains Malaysia, 11800 USM Penang, Pulau Pinang, Malaysia

Received: 28 September 2018/Accepted: 22 April 2019

ABSTRAK

Kegagalan cerun merupakan suatu fenomena disebabkan hujan yang sering berlaku di kawasan tropika seperti Malaysia. Kertas ini menghuraikan penggunaan Sistem Maklumat Geografi (GIS) dan Analisis Diskriminan untuk memodelkan ciri-ciri fizikal kegagalan cerun serta hubung kait statistik kejadian kegagalan cerun dengan parameter fizikal yang menyumbang kepada kejadian kegagalan cerun di Pulau Pinang. Analisis diskriminan adalah satu kaedah analisis yang boleh digunakan untuk mendiskriminasikan sesuatu kumpulan kegagalan cerun berdasarkan parameter tertentu. Tujuan utama analisis ini dijalankan adalah bagi memahami faktor yang mempengaruhi perbezaan antara kumpulan kegagalan cerun dan cerun stabil (tiada kegagalan cerun), seterusnya membuat ramalan tentang kemungkinan terjadi sesuatu kegagalan cerun. Oleh yang demikian, satu kombinasi linear pemboleh ubah bebas telah dibentuk dan digunakan sebagai asas dalam mengkelaskan kes kegagalan cerun tertentu. Kajian ini menggunakan sepuluh pemboleh ubah iaitu jarak ke jalan, purata hujan tahunan, litologi batuan, ketinggian topografi, kecuraman cerun, siri tanih, aspek cerun, jarak ke sungai, jenis guna tanah dan lineamen. Model yang terhasil didapati berjaya meramal 92.5% daripada kejadian kegagalan cerun sebenar. Model yang dibentuk kemudiannya telah dinilai menggunakan 30% daripada sampel kejadian sebenar dan menghasilkan ketepatan sebanyak 91.24%.

Kata kunci: Analisis diskriminan; kegagalan cerun; pemodelan ruangan; Pulau Pinang; Sistem Maklumat Geografi

ABSTRACT

Slope failure is a phenomenon due to frequent rainfall that occurs in tropical areas such as Malaysia. This paper describes the use of Geographical Information Systems (GIS) and Discriminant Analysis to model the physical features of slope failure and the statistical association between slope failure events with physical parameters that contribute to the incidence of slope failure in Pulau Pinang. Discriminant analysis is an analysis method that can be used to discriminate against a set of slope failures based on certain criteria. The main purpose of this analysis were to understand the factors that affect the difference between the group of slope failure and subsequently making predictions about a possible slope failure. Therefore, a linear combination of independent variables has been formed and used as a basis for classifying certain slope failure cases. The study used ten variables: distance to the road, average annual rainfall, lithology, topography height, slope gradient, soil series, slope aspect, distance to river, landuse type and lineament. The resulting model was able to predict 92.5% of actual slope failure events. The model was validated using 30% of the actual incident samples and found 91.24% accuracy.

Keywords: Discriminant analysis; Geographical Information System; Pulau Pinang; slope failure; spatial modeling

REFERENCES

Akgul, A. & Bulut, F. 2007. GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ. Geol. 51(8): 1377-1387.

Akgun, A., Dag, S. & Bulut, F. 2008. Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology 54(6): 1127- 1143.

Aleotti, P. & Chowdhury, R. 1999. Landslide hazard assessment: Summary review and new perspectives. Bull. Eng. Geol. Env. 58(1): 21-44.

Althuwaynee, O.F. & Pradhan, B. 2017. Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City, Geomatics, Natural Hazards and Risk 8(2): 706-732. DOI: 10.1080/19475705.2016.1255670.

Atkinson, P.M. & Massari, R. 1998. Generalized linear modeling of susceptibility to landsliding in the central Apennines, Italy. Comput. Geosci. 24(4): 373-385.

Ayalew, L. & Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1): 15-31.

Ayele, S., Raghuvanshi, T.K. & Kala, P.M. 2014. Application of remote sensing and GIS for landslide disaster management: A case from Abay Gorge, Gohatsion-Dejen section, Ethiopia. In Landscape Ecology and Water Management. Tokyo: Springer. hlm. 15-32.

Baecher, G. & Christian, J. 2003. Reliability and Statistics in Geotechnical Engineering. 1st ed. Chichester: John Wiley.

Baeza, C. & Corominas, J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Process & Landform 26(12): 1251-1263.

Bates, R.L. & Jackson, J.A. 1987. Glossary of Geology. Alexandria, Virginia: American Geological Institute. p. 788.

Ben Slimane, A., Raclot, D., Evrard, O., Sanaa, M., Lefèvre, I. & Le Bissonnais, Y. 2015. Relative contribution of rill/ interrill and gully/channel erosion to small reservoir siltation in mediterranean environments. Land Degradation & Development 27(3): 785-797. DOI: 10.1002/ldr.2387.

Beven, K.J. & Kirkby, M.J. 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24: 43-69.

Borga, M., Tonelli, F., dalla Fontana, G. & Cazorzi, F. 2005. Evaluating the influence of forest roads on shallow landsliding. Ecol. Model. 187: 85-98.

Bromley, D.W. 1971. The use of discriminant analysis in selecting rural development strategies. American Journal of Agricultural Economics 53(2): 319-322.

Budimir, M.E.A., Atkinson, P.M. & Lewis, H.G. 2015. A systematic review of landslide probability mapping using logistic regression. Landslides 12(3): 419-436.

Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I. & Dick, O.B. 2012. Landslide susceptibility assessment in the Hoa Binh Province of Vietnam: A comparison of the Levenberg Marquardt and Bayesian regularized neural networks. Geomorphology doi:10.1016/ j.geomorph.

Caniani, D., Pascale, S., Sdao, F. & Sole, A. 2007. Neural networks and landslide susceptibility: A case study of the urban area of potenza. Natural Hazards 45: 55-72.

Carrara, A., Cardinali, M., Guzzetti, F. & Reichenbach, P. 1995. GIS technology in mapping landslide hazard. Geographical Information Systems in Assessing Natural Hazards. Netherlands: Springer. pp. 135-175.

Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V. & Reichenbach, P. 1991. GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms 16(5): 427-445.

Carro, M., de Amicis, M., Luzi, L. & Marzorati, S. 2003. The application of predictive modeling techniques to landslides induced by earthquakes, the case study of the 26 September 1997 Umbria-Marche Earthquake (Italy). Eng. Geol. 69: 139-159.

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

Chapin, F.S. & Kaiser, E.J. 1979. Urban Land Use Planning. Urbana: University of Illinois Press.

Chen, H., Lin, G.W., Lu, M.H., Shih, T.Y., Horng, M.J. & Wu, S.J. 2011. Effects of topography, lithology, rainfall and earthquake on landslide and sediment discharge in mountain catchments of Southeastern Taiwan. Geomorphology 133: 132-142.

Choi, J., Oh, H.J., Won, J.S. & Lee, S. 2010. Validation of an artificial neural network model for landslide susceptibility mapping. Environmental Earth Sciences 60(3): 473-483. https://doi.org/10.1007/s12665-009-0188-0.

Chung, C.J.F., Fabbri, A.G. & Van Westen, C.J. 1995. Multivariate regression analysis for landslide hazard zonation. Geographical Information Systems in Assessing Natural Hazards. Netherlands: Springer. pp. 107-133.

Clerici, A., Perego, S., Tellini, C. & Vescovi, P. 2002. A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48(4): 349-364.

Conforti, M., Aucelli, P.P., Robustelli, G. & Scarciglia, F. 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (northern Calabria, Italy). Nat. Hazards. 56(3) 881-898.

Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S. & Paudyal, P. 2008. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weight of evidence. Geomorphology 102: 496-510.

Dai, F.C. & Lee, C.F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3): 213-228.

Dai, F.C. & Lee, C.F. 2001. Frequency volume relation and prediction of rainfall-induced landslides. Eng. Geol. 59(3- 4): 253-266.

Dong, J.J., Tsao, C.C., Yang, C.M., Wu, W.J., Lee, C.T., Lin, M.L., Zhang, W.F., Pei, X.J., Wang, G.H. & Huang, R.Q. 2017. The geometric characteristics and initiation mechanisms of the earthquake-triggered Daguangbao landslide. In Geotechnical Hazards from Large Earthquakes and Heavy Rainfalls, edited by Hazarika, H., Kazama, M. & Lee, W. Tokyo: Springer. hlm. 203-213.

Dragicevic, S., Lai, T. & Balram, S. 2015. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat International 45: 114-125.

Ercanoglu, M. & Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology 41(6): 720-730.

Fatimah Shafinaz, A. 2005. Penggunaan sistem maklumat geografi untuk meramal keruntuhan cerun di Pulau Pinang. Tesis Ijazah Sarjana Kejuruteraan Awam Universiti Teknologi Malaysia (Tidak diterbitkan).

Field, A. 2009. Discovering Statistics using SPSS. 3rd ed. Los Angeles: SAGE Publications Ltd.

Frattini, P., Crosta, G., Carrara, A. & Agliardi, F. 2008. Assessment of rockfall susceptibility by integrating statistical and physically based approaches. Geomorphology 94: 419- 437.

Gerrard, A.J. 1981. Soil and landforms: An integration of geomorphology and pedology. Deparment of Geography, University of Birmigham (Unpublished).

Gessesse, B., Bewket, W. & Brauning, A. 2015. Model-based characterization and monitoring of runoff and soil erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia. Land Degrad. Dev. 26: 711-724. doi: 10.1002/ldr.2276.

Gigovic, L., Drobnjak, S. & Pamucar, D. 2019. The application of the hybrid GIS spatial multi-criteria decision analysis best-worst methodology for landslide susceptibility mapping. International Journal of Geo-Information (ISPRS) 8(79): 1-29.

Gokceoglu, C., Sonmez, H. & Ercaglu, M. 2000. Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Engineering Geology 55: 227-296.

Gorsevski, P.V., Gessler, P. & Foltz, R.B. 2000. Spatial prediction of landslide hazard using discriminant analysis and GIS. GIS in the Rockies 2000 Conference and Workshop Applications for the 21st Century. Denver, Colorado. September 25-27.

Guo, C., Montgomery, D.R., Zhang, Y., Wang, K. & Yang, Z. 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248: 93-100.

Guo-liang, D., Zhang, Y.S. & Iqbal, J. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science 14(2): 249-268. DOI: 10.1007/s11629- 016-4126-9.

Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F. & Cardinali, M. 2006. Landslide hazard assessment in the Collazzone area, Umbria, Central Italy. Natural Hazards and Earth System Sciences 6: 115-131.

Guzzetti, F., Carrara, A., Cardinali, M. & Reichenbach, P. 1999. Landslide evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31: 181-216.

Guzzetti, F., Cardinali, M. & Reichhenbach, P. 1994. The AVI Project: A bibliographical and archive inventory of landslides and floods in Italy. Environmental Management 18(4): 623- 633.

Gupta, S.K., Shukla, D.P. & Thakur, M. 2018. Selection of weightages for causative factors used in preparation of landslide susceptibility zonation (LSZ). Geomatics, Natural Hazards and Risk 9(1): 471-487.

Hair, J.F., Anderson, E.R., Tatham, R.L. & Black, W.C. 1992. Multivariate Data Analysis with Reading. Edisi Ketiga. New York: Macmillan Publishing Company.

Haregeweyn, N., Poesen, J., Verstraeten, G., Govers, G., Vente, J., Nyssen, J., Deckers, J. & Moeyersons, J. 2013. Assessing the performance of a spatially distributed soil erosion and sediment delivery model (watem/sedem) in Northern Ethiopia. Land Degrad. Develop. 24: 188-204. doi:10.1002/ ldr.1121.

Hong, H., Naghibi, S.A., Dashtpagerdi, M.M., Pourghasemi, H.R. & Chen, W. 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab. J. Geosci. 10: 167.

Hong, H., Pourghasemi, H.R. & Pourtaghi, Z.S. 2016. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259: 105-118.

IBM. 2016. Discriminant Analysis. http://www.ibm.com/support/ knowledgecenter/SSLVMB_22.0.0/com.ibm.spss.statistics. help/spss/base/idh_disc.htm.

Ibrahim Komoo. 1989. Engineering Geology of Kuala Lumpur, Malaysia. Proc. Int. Conf. Eng. Geology in Tropica Terrain. Kuala Lumpur. hlm. 262-273.

Ibrahim Abdullah & Juhari Mat Akhir. 1990. Basic Dictionary of Geological Terms. Bangi: Universiti Kebangsaan Malaysia.

Jaafari, A., Najari, A., Rezaeian, J., Sattarian, A. & Ghajar, I. 2015. Planning road network in landslide-prone areas: A case study from the Northern Forests of Iran. Land Use Policy 47: 198-208.

Jaafari, A., Najafi, A., Rezaeian, J. & Sattarian, A. 2014. Modeling erosion and sediment delivery from unpaved roads in the north mountainous forest of Iran. GEM - Int. J. Geomath. 6(2): 343-356.

Jabatan Perangkaan Malaysia. 2013. Maklumat Asas Negeri Pulau Pinang. Jabatan Pemetaan Malaysia.

Kai, X., Qiang, G., Zhengwei, L., Jie, X., Yanshan, Q. & Chunfang, K. 2015. Landslide susceptibility evaluation based on BPNN and GIS: A Case of Guojiaba in The Three Gorges Reservoir Area. International Journal of Geographical Information Science 29(7): 1111-1124.

Kanungo, D.P., Arora, M.K., Sarkar, S. & Gupta, R.P. 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.

Kazmi, D., Qasim, S., Harahap, I.S.H., Baharom, S., Imran, M. & Moin, S. 2016. A study on the contributing factors of major landslides in Malaysia. Civil Engineering Journal 2(12): 669-678.

Klecka, W. 1980. Discriminant Analysis. California: Sage Publication.

Klose, M., Gruber, D., Damn, B. & Gerold, G. 2014. Spatial databases and GIS as a tools for regional landslide susceptibility modeling. Zeitschrift für Geomorphologie58(1): 1-36.

Komac, M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1): 17-28.

Kou, M., Jiao, J., Yin, Q., Wang, N., Wang, Z., Li, Y. & Cao, B. 2016. Successional trajectory over 10 years of vegetation restoration of abandoned slope croplands in the hill-gully region of the loess plateau. Land Degradation & Development 27(4): 919-932.

Lamelas, M.T., Marinoni, O., Hoppe, A. & Riva, J. 2008. Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain). Environ. Geol. 54(5): 963-977.

Lan, H.X., Zhou, C.H., Wang, L.J., Zhang, H.Y. & Li, R.H. 2004. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China. Engineering Geology 76(1): 109-128.

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

Lee, S. & Pradhan, B. 2006. Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science 115(6): 661-672.

Lee, S. & Jasmi, A.T. 2005. Probabilistic landslide susceptibility and factor effect analysis. Environ. Geol. 47: 982-990.

Lee, S. & Min, K. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Env. Geol. 40: 1095-1113.

Leoi, S., Chan, A. & Trisha, N 2018. Malaysia among countries especially prone to landslides. The Star. 4 Dec.

Lin, H.M., Chang, S.K., Wu, J.H. & Juang, C.H. 2009. Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation. Engineering Geology 104(3-4): 280-289.

Lin, M.L., Lin, S.C. & Lin, Y.C. 2016. Review of landslide occurrence and climate change in Taiwan. In Slope Safety Preparedness for Impact of Climate Change, edited by Ho, K., Lacasse, S. & Picarelli, L. London: CRC Press. hlm. 409-436. 10.1201/9781315387789-14.

Mark, R.K. & Ellen, S.D. 1995. Statistical and simulation models for mapping debris-flow hazard. In Geographical Information Systems in Assessing Natural Hazards. Netherlands: Springer. pp. 93-106.

Montgomery, C.W. 1997. Env. Geol. 5th ed. New York: WCB McGraw-Hill Co.

Moore, I.D. & Gallant, J.C. 1991. Overview of hydrologic and water quality modeling. Modeling the Fate of Chemicals in the Environmental, edited by Moore, I.D. Canberra: Center for Resource and Environmental Studies, the Australian National University. hlm. 1-8.

Morrison, D.G. 1967. On the interpretation of discriminant analysis. Journal of Marketing Research 6(2): 156-163.

Moses, C. & Robinson, D. 2011. Chalk coast dynamics: Implications for understanding rock coast evolution. Earth- Science Reviews 109(3-4): 63-73.

Mustafa Kamal. 2007. Climate change - Its effects on the agricultural sector in Malaysia. Paper presented at National Seminar on Socio-Economic Impact of Extreme Weather and Climate Change, organized by the Ministry of Science, Technology and Innovation, Putrajaya, Malaysia. 21- 22 June.

Mwaniki, M.W., Moeller, M.S. & Schellmann. 2015. A comparison of Landsat (OLI) and landsat & (ETM+) in mapping geology and visualizing lineament: A case study of central region Kenya. International Symposium on Remote Sensing of Environment. 11-15 May, Berlin, Germany.

Nagarajan, R., Roy, A., Kumar, R.V., Mukherjee, A. & Khire, M.V. 2000. Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull. of Eng. Geol. and Env. 58(4): 275-287.

Nandi, A. & Shakoor, A. 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng. Geol. 110(1-2): 11-20.

Nicholas, K.C. 1995. Geohazards: Natural and Human. New Jersey: Prentice Hall.

Norusis, M.J. 1993. SPSS: SPSS for Windows, Base System User’s Guide Release 6.0. SPSS Inc.

Nuriah, A.M., Ruslan, R. & Wan, M.M.W.I. 2018. Pemodelan ruangan pelbagai jenis kegagalan cerun menggunakan rangkaian saraf buatan (ANN) di Pulau Pinang, Malaysia. Jurnal Teknologi 80(4): 135-146.

Nuriah, A.M., Ruslan, R. & Wan, M.M.W.I. 2017. Pemodelan ruangan pelbagai jenis kegagalan cerun di Pulau Pinang menggunakan kaedah nisbah kekerapan. Geografi 5(2): 13-26.

Nuriah, A.M., Ruslan, R. & Wan, M.M.W.I. 2016. Analisis taburan dan corak ruangan pelbagai jenis kegagalan cerun di Pulau Pinang, Malaysia. International Journal of Environment, Society and Space 4(2): 1-15.

Ohlmacher, G.C. & Davis, J.C. 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansa, USA. Eng. Geol. 2157: 1-13.

Oh, H.J. & Pradhan, B. 2011. Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in a tropical hilly area. Comput. Geosci. 37: 1264-1276.

Pachauri, A.K., Gupta, P.V. & Chander, R. 1998. Landslide zoning in a part of the Garhwal Himalayas. Environ. Geol. 36(3-4): 325-334.

Pham, B.T., Tien Bui, D., Pourghasemi, H.R., Indra, P. & Dholakia, M.B. 2017. Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor. Appl. Climatol. 128: 255-273.

Piegari, E., Cataudella, V., Di Maio, R., Milano, L., Nicodemi, M. & Soldovieri, M.G. 2009. Electrical resistivity tomography and statistical analysis in landslide modelling: A conceptual approach. Journal of Applied Geophysics 68(2): 151-158.

Peng, M. & Zhang, L.M. 2012. Breaching parameters of landslide dams. Landslides 9(1): 13-31.

Petley, D. 2017a. George Town: Another serious landslide in Penang. The Landslide Blog - AGU Blogosphere. 5 November 2017. Diakses pada 1 April 2019.

Petley, D. 2017b. The Tanjung Bungah landslide: A very challenging site. The Landslide Blog - AGU Blogosphere. 24 Oktober 2017. Diakses pada 1 April 2019.

Petley, D. 2018. George Town: Another major fatal landslide in Penang, Malaysia. The Landslide Blog - AGU Blogosphere. 24 Oktober 2018. Diakses pada 1 April 2019.

Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C. & Gokceoglu, C. 2013. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science 122(2): 349-369.

Pourtaghi, Z.S. & Pourghasemi, H.R. 2014. GIS-based groundwater spring potential assessment and mapping in the birjand township, southern Khorasan province, Iran. Hydrogeol. J. 22(3): 643-662.

Pradhan, B. 2010. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research 45(10): 1244-1256.

Pradhan, B. & Buchroithner, M.F. 2010. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environmental & Engineering Geoscience 16(2): 107-126.

Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, M. & Khujaimah, Z. 2008. Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. J Appl Remote Sens 2: 1-11.

Raja, N.B., Cicek, I., Turkoglu, N., Aydin, O. & Kawasaki, A. 2017. Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Nat. Hazards. 85: 1323-1346.

Ramos-Canon, A.M., Prada-Sarmiento, L.F., Trujillo-Vela, M.G., Macías, J.P. & Santos-R, A.C. 2016. Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia. Landslides 13: 671-681.

Romeo, R. 2000. Seismically induced landslide displacements: A predictive model. Eng. Geol. 58: 337-351.

Rece, A. & Capolongo, D. 2002. Probabilistic modeling of uncertainties in earthquakeinduced landslide hazard assessment. Comput. Geosci. 28: 735-749.

Sahoo, S. 2009. A semi quantitative landslide susceptibility assessment using logistic regression model and rock mass classification system: Study in a part of Uttarakhand Himalaya, India. Thesis Master of Science. International Institute for Geo-Information Science and Earth Observation Enshede the Netherlands (Tidak diterbitkan).

Sarkar, S. & Kanungo, D.P. 2004. An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogram Eng. Remote Sensing 70(5): 617-625.

Sharma, L.P., Patel, N., Ghose, M.K. & Debnath, P. 2014. Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: A case study from the Sikkim Himalayas in India. Geocarto International 29(2): 128-146.

Shou, K.J. & Yang, C.M. 2015. Predictive analysis of landslide susceptibility under climate change conditions - A study on the Chingshui River Watershed of Taiwan. Engineering Geology 192: 46-62.

Simon, N., Juhari Mat Akhir, Azlikamil Napiah & Kee, T.H. 2009. Pemetaan potensi bencana tanah runtuh menggunakan faktor penilaian bencana tanah runtuh dengan pendekatan GIS. Geological Society of Malaysia 55: 47-53.

Suzen, M.L. & Kaya, B.S. 2011. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. International Journal of Digital Earth 5: 338-355.

Suzen, M.L. & Doyuran, V.A. 2004. Comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate. Environ. Geol. 45: 665-679.

Tjia, H.D. 1987. Geomorfologi. Kuala Lumpur: Dewan Bahasa dan Pustaka.

Tsangaratos, P. & Ilia, I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides 13: 305-320.

Tunusluoglu, M.C., Gokceoglu, C., Nefeslioglu, H.A. & Sonmez, H. 2008. Extraction of potential debris source areas by logistic regression technique: A case study from Barla, Besparmak and Kapi Mountains (NW Taurids, Turkey). Environ. Geol. 54(1): 9-22.

Vahidnia, M.H., Alesheikh, A.A., Alimohammadi, A. & Hosseinali, F. 2010. A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput. Geosci. 36: 1101-1114.

Van Westen, C.J., Rengers, N. & Soeters, R. 2003. Use of geomorphological information in indirect landslide susceptibility assessment. Nat. Hazards 30: 399-413.

Wang, H.B. & Sassa, K. 2005. Comparative evaluation of landslide susceptibility in Minamata area, Japan. Environmental Geology 47(7): 956-966.

Wan Mohd Muhiyuddin, W.I. 2005. Pembentukan model ruangan kegagalan cerun bagi sub lembangan hulu Sungai Langat. Tesis PhD. Universiti Sains Malaysia (Tidak diterbitkan).

Yalcin, A. 2008. GIS-Based landslides susceptibility mapping using analytical hierarchy process and bivariate statistic in Ardesen (Turkey): Comparisons of results and confirmations. Catena. 72(1): 1-12.

Yalcin, A. & Bulut, F. 2007. Landslide susceptibility mapping using gis and digital photogrammetric techniques: A case study from Ardesen (NE-Turkey). Nat. Hazards 41: 201-226.

Yesilnacar, E. & Topal, T. 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.

Youssef, A.M., Pradhan, B., Gaber, A.F.D. & Buchroithner, M.F. 2009. Geomorphological hazard analysis along the Egyptian red sea coast between Safaga and Quseir. Natural Hazards and Earth System Sciences 9(3): 751-766.

Zezere, J.L., Pereira, S., Tavares, A.O., Bateira, C., Trigo, R.M., Quaresma, I., Santos, P.P., Santos, M. & Verde, J. 2014. Disaster: A GIS database on hydro-geomorphologic disasters in Portugal. Nat. Hazards 72: 503-532.

Zhou, G., Esaki, T., Mitani, Y., Xie, M. & Mori, J. 2003. Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach. Engineering Geology 68(3): 373-386.

*Corresponding author; email: nuriah@ukm.edu.my