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Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping

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Abstract

This study compares the predictive performance of GIS-based landslide susceptibility mapping (LSM) using four different kernel functions in support vector machines (SVMs). Nine possible causal criteria were considered based on earlier similar studies for an area in the eastern part of the Khuzestan province of southern Iran. Different models and the resulting landslide susceptibility maps were created using information on known landslide events from a landslide inventory dataset. The models were trained using landslide inventory dataset. A two-step accuracy assessment was implemented to validate the results and to compare the capability of each function. The radial basis function was identified as the most efficient kernel function for LSM with the resulting landslide susceptibility map showing the highest predictive accuracy, followed by the polynomial kernel function. According to the obtained results, it concluded that using SVMs can generally be considered to be an effective method for LSM while it demands careful consideration of kernel function. The results of the present research will also assist other researchers to select the best SVM kernel function to use for LSM.

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References

  • Abe S (2010) Two-class support vector machines. In Support vector machines for pattern classification, Advances in Pattern Recognition. Springer, London, pp 21–112

    Google Scholar 

  • 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(1/2):15–13

    Article  Google Scholar 

  • Bai SB, Wang J, Lu GN, Kanevski M, Pozdnoukhov A (2008) GIS-based landslide susceptibility mapping with comparisons of results from machine learning methods versus logistic regression in basin scale, Geophysical Research Abstracts, EGU, vol. 10, A-06367

  • Bak M (2009) Support vector classifier with linguistic interpretation of the kernel matrix in speaker verification, man-machine interactions, Krzysztof A. Cyran, Stanislaw Kozielski, James F. Peters (eds.), ISSN 1867–5662, Springer, 2009, 399–406

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River basin case study, Italy. Math Geosci 44(1):47–70

    Article  Google Scholar 

  • Ben-Hur A, Weston J (2010) A user’s guide to support vector machines. Methods Mol Biol 609:223–239

    Article  Google Scholar 

  • Boser BE, IM Guyon, VN Vapnik (1992) A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual ACM Work-shop on Computational Learning Theory, pp. 144–152. ACM Press

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards Earth System Science 5:853–862

    Article  Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Landslide susceptibility assessment in the Hoa Binh province of Vietnam using artificial neural network. Geomorphology. doi:10.1016/ j.geomorph.2012.04.023

    Google Scholar 

  • Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Spatial prediction of landslide hazards in Vietnam: a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Campbell W, D Sturim, D Reynolds, A Solomonoff (2006) SVM based speaker verification using a GMM supervector kernel and NAP variability compensation, in ICASSP, vol. 1, pp 97–100

  • Chen W, Xie X, Wang J,Pradhan B, Hong H, Bui DT, Ma J (2016) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility, doi.org/10.1016/j.catena.2016.11.032

  • Elshinawy MY, AHA Badawy, WW Abdelmageed, MF Chouikha. (2010) Comparing one-class and two-class SVM classifiers for normal mammogram detection, IEEE Applied Imagery Pattern Recognition Workshop. DOI: 10.1109/AIPR.2010.5759708

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75(3–4):229–250

    Article  Google Scholar 

  • Faraji Sabokbar H, Shadman Roodposhti M, Tazik E (2014) Landslide susceptibility mapping using geographically-weighted principal component analysis. Geomorphology. doi:10.1016/j.geomorph.2014.07.026

    Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2011) Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. J Earth Scie Eng 1(1):66–71

    Google Scholar 

  • Feizizadeh B, T Blaschke (2012) Uncertainty analysis of GIS-based ordered weighted averaging method for landslide susceptibility mapping in Urmia Lake Basin, Iran International Conference of GIScience 2012, Columbus, Ohio, USA, September, 18–21, 2012

  • Feizizadeh B, Blaschke T (2013a) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2013b) Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. J Environ Plan Manag 56:1–23

    Article  Google Scholar 

  • Feizizadeh B, Kienberger S (2017) Spatial explicit sensitivity and uncertainty analysis for multicriteria based vulnerability assessment. J Environ Plan Manag. doi:10.1080/09640568.2016.1269643

    Google Scholar 

  • Feizizadeh B, Blaschke T, Nazmfar H (2012) GIS-based ordered weighted averaging and Dempster Shafer methods for landslide susceptibility mapping in Urmia lake basin Iran. Int J Digital Earth. doi:10.1080/17538947.2012.749950

    Google Scholar 

  • Feizizadeh B, Blaschke T, Nazmafar H, Rezaei Mogadam MH (2013a) Landslide susceptibility mapping using GIS-based analytical hierarchical process for the Urmia Lake basin, Iran. Int J Environ Res 7(2):319–3336

    Google Scholar 

  • Feizizadeh B, Blaschke T, Shadman Roodposhti M (2013b) Integration of GIS based fuzzy set theory and multicriteria evaluation methods for landslide susceptibility mapping. Int J Geoinformatics 9(3):49–57

    Google Scholar 

  • Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci. doi:10.1016/j.cageo.2014.08.001

    Google Scholar 

  • Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Comput Geosci 36:1005–1020

    Article  Google Scholar 

  • Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35:21–141

    Google Scholar 

  • Gunn SR (1997) Support vector machines for classification and regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southampton, USA

  • He S, P Pan, L Dai, H Wang, J Liu (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China, Geomorphology, 171–172:30–41

  • Hong H, Pardahan B, Jebur MN, Bui DT, Akgun A (2015a) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Int J Environ Earth Sci 75:1–14

    Google Scholar 

  • Hong H, Pradhan B, Xu C, Bui DT (2015b) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines, Catena, 133, 266-281 Jaafari, A., A

  • Hong HY, Pourghasemi HR and Pourtaghi ZS (2016a) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models, Geomorphology, DOI: 10.1016/j.geomorph.2016.02.012

  • Hong H, Pradhan, Bui DT, Xu C, Youssef AM, Chen W (2016b) Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China), doi.org/10.1080/19505.2016.1250112

  • Hong H, Chen w, Xu C, Youssef AM, Pradhan B, Bui DT (2016c) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy, doi.org/10.1080/1049.2015.1130086

  • Hsu CW, CC Chang, CJ Lin. (2010) A practical guide to support vector classification, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei

  • Hyndman JR, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688

    Article  Google Scholar 

  • Jaafari A, Najafi HR, Pourghasemi J, Rezaeian AS (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol. doi:10.1007/s13762-013-0464-0

    Google Scholar 

  • Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

    Article  Google Scholar 

  • Kayastha P, Dhital M, De Smedt F (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat Hazards 63(2):479–498

    Article  Google Scholar 

  • Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslide 4(4):327–338

    Article  Google Scholar 

  • Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2:139–154

    Google Scholar 

  • Micheletti N (2011) Landslide susceptibility mapping using adaptive support vector machines and feature selection, A Master thesis submitted to University of Lausanne Faculty of Geosciences and Environment for the Degree of Master of Science in Environmental Geosciences, 99p

  • MNR, Ministry of Natural Resources, Khuzestan Province (2010) Landslide event report, Khuzestan, Iran

  • Moradi M, Bazyar MH, Mohammadi Z (2012) GIS-based landslide susceptibility mapping by AHP method, a case study, Dena City, Iran. J Basic Appl Sci Res 2(7):6715–6723

    Google Scholar 

  • Muñoz-Marí J, Bovolo F, Gómez-Chova L, Bruzzone L, Camps-Valls G (2010) Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Trans Geosci Remote Sens 48(8):3188–3197

    Article  Google Scholar 

  • Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1–2):11–20

    Google Scholar 

  • Ozdemir A (2011) Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey). Nat Hazards 59(3):1573–1607

    Article  Google Scholar 

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

  • 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. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data for the development of fuzzy algebraic operators to landslide hazard mapping: a case study in Malaysia. Applied Geomatics 1:3–15

    Article  Google Scholar 

  • Richards JA, Jia X (2006) Remote sensing digital image analysis. Springer-Verlag, Berlin, p 240

    Google Scholar 

  • Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1472

    Article  Google Scholar 

  • Senf A, Chen X, Zhang A (2006) Comparison of one-class SVM and two-class SVM for fold recognition. In ICONIP 2:140–149

    Google Scholar 

  • Shadman Roodposhti M, Rahimi S, Jafar Beglou M (2014) PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat Hazards 73(1):77–95

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  Google Scholar 

  • Tax DMJ, Duin RPW (1999) Support vector domain description. Pattern Recogn Lett 20:1191–1199

    Article  Google Scholar 

  • Tsangaratos P, Ilia L, Hong H, Chen W, Xu C (2016) Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China, DOI: 10.1007/s10346-016-0769-4

  • Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York

    Book  Google Scholar 

  • Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146(1):70–80

    Article  Google Scholar 

  • Yalçın 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

    Article  Google Scholar 

  • Yao X, Thamb LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582

    Article  Google Scholar 

  • Yapo PO, Gupta HV, Sorooshian S (1996) Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. J Hydrol 181(1–4):23–48

    Article  Google Scholar 

  • 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). Eng Geol 79(3–4):251–266

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Science 61:821–836

    Article  Google Scholar 

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Feizizadeh, B., Roodposhti, M.S., Blaschke, T. et al. Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10, 122 (2017). https://doi.org/10.1007/s12517-017-2918-z

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