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
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
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
Ben-Hur A, Weston J (2010) A user’s guide to support vector machines. Methods Mol Biol 609:223–239
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
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
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
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
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
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2:139–154
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
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
Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1–2):11–20
Ozdemir A (2011) Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey). Nat Hazards 59(3):1573–1607
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
Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30
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
Richards JA, Jia X (2006) Remote sensing digital image analysis. Springer-Verlag, Berlin, p 240
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
Senf A, Chen X, Zhang A (2006) Comparison of one-class SVM and two-class SVM for fold recognition. In ICONIP 2:140–149
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
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Tax DMJ, Duin RPW (1999) Support vector domain description. Pattern Recogn Lett 20:1191–1199
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
Vapnik VN (1998) Statistical learning theory. Wiley, New York
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
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
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
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
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
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
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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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DOI: https://doi.org/10.1007/s12517-017-2918-z