GIS-based support vector machine model in shallow landslide hazards prediction: A case study on Ilam dam watershed, Iran

Subject Areas : Hazard

Yaghoub Niazi ¹ , Manuel E Mendoza ² , Ali Talebi ³ , Hasti Bidaki ⁴

1 - MAGTA Development Center Company
2 - Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Mexico
3 - Faculty of Natural Resources, Yazd University, Yazd, Iran
4 - Faculty of Natural Resources, Yazd University, Yazd, Iran

Received: 2021-02-09 Accepted : 2021-03-03 Published : 2021-03-01

Keywords:

References:

Abe, S. (2010). Support vector machines for pattern classification. Springer 2010, pp 435.

Aditian, A., Kubota, T., & Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111.https://doi.org/10.1016/j.geomorph.2018.06.006

Akgun, A., & Türk, N. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environmental Earth Sciences, 61(3), 595-611. https://doi.org/10.1007/s12665-009-03731

Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120 135. https://doi.org/10.1016/j.cageo.2012.03.003

Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical geosciences, 44(1), 47 70. https://doi.org/10.1007/s11004-011-9379-9

Bednarik, M., Yilmaz, I., & Marschalko, M. (2012). Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’landslide area in south-west Slovakia. Natural hazards, 64(1), 547-575.https://doi.org/10.1007/s11069-012-02577

Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Journal, 24(1), 43-69. https://doi.org/10.1080/02626667909491834

Bonham-Carter, G.F. (2002). Geographic information systems for geoscientists: Modelling with GIS. In: Merriam, D.F. (Ed.). Computer Methods in the Geosciences, vol. 13. Pergamon/Elsevier, New York, pp. 302–334.

Brenning, A. (2005). Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6), 853-862.https://doi.org/10.5194/nhess-5-853-2005

Bucci, F., Santangelo, M., Cardinali, M., Fiorucci, F., & Guzzetti, F. (2016). Landslide distribution and size in response to Quaternary fault activity: the Peloritani Range, NE Sicily, Italy. Earth Surface Processes and Landforms, 41(5), 711-720.https://doi.org/10.1002/esp.3898

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. https://doi.org/10.1007/s10346-014-0550-5

Bui, D. T., Pradhan, B., Lofman, O., & Revhaug, I. (2012a). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical problems in Engineering, 2012. https://doi.org/10.1155/2012/974638

Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012b). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45, 199-211. https://doi.org/10.1016/j.cageo.2011.10.031

Bui, D. T., Tuan, T. A., Hoang, N. D., Thanh, N. Q., Nguyen, D. B., Van Liem, N., & Pradhan, B. (2017). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447-458. https://doi.org/10.1007/s10346-016-0711-9

Bui, D. T., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361-378. https://doi.org/10.1007/s10346-015-0557-6

Burrough, P. A. (1986). Principles of geographical. Information systems for land resource assessment. Clarendon Press, Oxford. https://doi.org/10.1080/10106048609354060

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. https://doi.org/10.1002/esp.3290160505

Chen, W., Pourghasemi, H. R., Panahi, M., Kornejady, A., Wang, J., Xie, X., & Cao, S. (2017). Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology, 297, 69-85.https://doi.org/10.1016/j.geomorph.2017.09.007

Chung, C. J. F., & Fabbri, A. G. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric engineering and remote sensing, 65(12), 1389-1399.

Chung, C. J. F., Fabbri, A. G., & Van Westen, C. J. (1995). Multivariate regression analysis for landslide hazard zonation. In Geographical information systems in assessing natural hazards (pp. 107-133). Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8404-3_7

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.https://doi.org/10.1016/S0169-555X(02)00079-X

Colkesen, I., Sahin, E. K., & Kavzoglu, T. (2016). Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. Journal of African Earth Sciences, 118, 53-64.https://doi.org/10.1016/j.jafrearsci.2016.02.019

Cooke, R. V., & Doornkamp, J. C. (1990). Geomorphology in environmental management: a new introduction. Oxford University Press (OUP).

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273 297.https://doi.org/10.1007/BF00994018

Dahal, R. K., Hasegawa, S., Nonomura, A., Yamanaka, M., Masuda, T., & Nishino, K. (2008). GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54(2), 311-324. https://doi.org/10.1007/s00254-007-0818-3

Dai, F. C., Lee, C. F., Li, J. X. Z. W., & Xu, Z. W. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381-391.https://doi.org/10.1007/s002540000163

Damaševičius, R. (2010). Optimization of SVM parameters for recognition of regulatory DNA sequences. Top, 18(2), 339-353. https://doi.org/10.1007/s11750-010-0152-x

DeGraff, J. V. (1979). Initiation of shallow mass movement by vegetative-type conversion. Geology, 7(9), 426-429. https://doi.org/10.1130/0091-7613(1979)72.0.CO;2

Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., & Althuwaynee, O. F. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural hazards, 65(1), 135-165. https://doi.org/10.1007/s11069-012-0347-6

Ercanoglu, M. U. R. A. T., Gokceoglu, C. A. N. D. A. N., & Van Asch, T. W. (2004). Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Natural Hazards, 32(1), 1-23.  https://doi.org/10.1023/B:NHAZ.0000026786.85589.4a

Ercanoglu, M., & Gokceoglu, C. (2003). Landslide Susceptibility Zoning of North Yenice (NW Turkey) by Multivariate Statistical Techniques. Natural Hazard 00(00):1-23. https://doi.org/10.1023/B:NHAZ.0000026786.85589.4a

Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4), 327-343. https://doi.org/10.1016/j.geomorph.2004.09.025

Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8), 861-874.https://doi.org/10.1016/j.patrec.2005.10.010

Fredlund, D.G. (1987). Slope stability analysis incorporating the effect of soil suction, in Anderson MG, and Richards KS, eds., Slope stability: geotechnical engineering and geomorphology: Chichester, UK. John Wiley & Sons, p. 113-144.

Gheshlaghi, H. A., & Feizizadeh, B. (2017). An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping. Journal of African Earth Sciences, 133, 15-24.  https://doi.org/10.1007/978-1-84996-098-4

Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196.https://doi.org/10.3390/rs11020196

Goetz, J. N., Guthrie, R. H., & Brenning, A. (2011). Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129(3-4), 376-386.https://doi.org/10.1016/j.geomorph.2011.03.001

Gordo, C., Zêzere, J. L., & Marques, R. (2019). Landslide susceptibility assessment at the basin scale for rainfall-and earthquake-triggered shallow slides. Geosciences, 9(6), 268.https://doi.org/10.3390/geosciences9060268

Graff. J., & Romesburg, H. (1980). Regional landslide-susceptibility assessment for wildland management: a matrix approach. In: Coates, D., Vitek, J. (Eds.). Thresholds in Geomorphology. George Allen and Unwin, London, pp. 401–414. https://doi.org/10.4324/9781003028697-19

Greenbaum, D., Tutton, M., Bowker, M. R., Browne, T. J., Buleka, J., Greally, K. B., & Tragheim, D. G. (1995). Rapid methods of landslide hazard mapping: Papua New Guinea case study.

Greenway, D.R. (1987). Vegetation and slope stability. In: Anderson MG, Richards KS (eds) Slope stability. Wiley, New York, pp 187–230.

Gritzner, M. L., Marcus, W. A., Aspinall, R., & Custer, S. G. (2001). Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology, 37(1-2), 149 165.  https://doi.org/10.1016/S0169-555X(00)00068-4

Günther, A., Van Den Eeckhaut, M., Malet, J. P., Reichenbach, P., & Hervás, J. (2014). Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology, 224, 69-85.https://doi.org/10.1016/j.geomorph.2014.07.011

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.https://doi.org/10.1016/S0169-555X(99)00078-1

Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1-2), 166 184. https://doi.org/10.1016/j.geomorph.2006.04.007

Hadi, A. I., Brotopuspito, K. S., Pramumijoyo, S., & Hardiyatmo, H. C. (2018). Regional Landslide Potential Mapping in Earthquake-Prone Areas of Kepahiang Regency, Bengkulu Province, Indonesia. Geosciences, 8(6), 219.

Hall, F. G., Townshend, J. R., & Engman, E. T. (1995). Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment, 51(1), 138-156.https://doi.org/10.1016/0034-4257(94)00071-T

He, H., Hu, D., Sun, Q., Zhu, L., & Liu, Y. (2019). A landslide susceptibility assessment method based on GIS technology and an AHP-weighted information content method: A case study of southern Anhui, China. ISPRS International Journal of Geo-Information, 8(6), 266.https://doi.org/10.3390/ijgi8060266

Hennrich, K., & Crozier, M. J. (2004). A hillslope hydrology approach for catchment‐scale slope stability analysis. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 29(5), 599-610.https://doi.org/10.1002/esp.1054

Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., & Ahmad, B. B. (2018). Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399-413. https://doi.org/10.1016/j.catena.2018.01.005

Hong, H., Pradhan, B., Xu, C., &Tien Bui, D. (2015). 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. https://doi.org/10.1016/j.catena.2015.05.019

Huang, Y., & Zhao, L. (2018). Review on landslide susceptibility mapping using support vector machines. Catena, 165, 520-529. https://doi.org/10.1016/j.catena.2018.03.003

Kalantar, B., Pradhan, B., Naghibi, S. A., Motevalli, A., & Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 9(1), 49-69. https://doi.org/10.1080/19475705.2017.1407368

Kanevski, M., Pozdnoukhov, A., & Timonin, V. (2009). Machine learning for spatial environmental data: theory, applications, and software. EPFL Press, Lausanne, p pp 275. https://doi.org/10.1201/9781439808085

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(3-4), 347-366. https://doi.org/10.1016/j.enggeo.2006.03.004

Kanungo, D.P., Arora, M.K., Sarkar, S., & Gupt, R.P. (2009). Landslide Susceptibility Zonation (LSZ) Mapping - A Review. Journal of South Asia Disaster Studies, Vol. 2 No. 1, 81- 105.

Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural computation, 15(7), 1667-1689. https://doi.org/10.1162/089976603321891855

Kornejady, A., Ownegh, M., & Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena, 152, 144-162. https://doi.org/10.1016/j.catena.2017.01.010

Kuriakose, S. L., Devkota, S., Rossiter, D. G., & Jetten, V. G. (2009). Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India. Catena, 79(1), 27-38. https://doi.org/10.1016/j.catena.2009.05.005

Kuriakose, S. L., Van Beek, L. P. H., & Van Westen, C. J. (2009). Parameterizing a physically based shallow landslide model in a data poor region. Earth surface processes and landforms, 34(6), 867-881. https://doi.org/10.1002/esp.1794

Lacasse, S., & Nadim, F. (2009). Landslide risk assessment and mitigation strategy. In Landslides–disaster risk reduction (pp. 31-61). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69970-5_3

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.

Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental geology, 40(9), 1095-1113.https://doi.org/10.1007/s002540100310

Lee, S., Chwae, U., & Min, K. (2002). Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology, 46(3-4), 149-162.https://doi.org/10.1016/S0169-555X(02)00057-0

Lee, S., Hong, S. M., & Jung, H. S. (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability, 9(1), 48. https://doi.org/10.3390/su9010048

Lee, S., Hwang, J., & Park, I. (2013). Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena, 100, 15-30. https://doi.org/10.1016/j.catena.2012.07.014

Lee, S., Ryu, J.H., Lee, M.J., & Won, J.S. (2003). Use of an artificial neural network for analysis of susceptibility to landslides at Boun, Korea. Environmental Geology 34 (1), 59–69. https://doi.org/10.1007/s00254-003-0825-y

Lin, G. W., Chen, H., Chen, Y. H., & Horng, M. J. (2008). Influence of typhoons and earthquakes on rainfall-induced landslides and suspended sediments discharge. Engineering Geology, 97(1-2), 32 41.  https://doi.org/10.1016/j.enggeo.2007.12.001

Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234. https://doi.org/10.1016/j.enggeo.2011.09.006

McDermid, G. J., & Franklin, S. E. (1995). Remote sensing and geomorphometric discrimination of slope processes. Zeitschrift für Geomorphologie. Supplementband, (101), 165-185.

Mohammady, M., Pourghasemi, H. R., & Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221-236. https://doi.org/10.1016/j.jseaes.2012.10.005

Moore, I. D., & Burch, G. J. (1986). Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resources Research, 22(8), 1350-1360. https://doi.org/10.1029/WR022i008p01350

Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. https://doi.org/10.1002/hyp.3360050103

Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264-1276. https://doi.org/10.1016/j.cageo.2010.10.012

Pachauri, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36(3), 325-334. https://doi.org/10.1007/s002540050348

Petley, D.N. (2008). The Global Occurrence of Fatal Landslides in 2007, Vol.10. Geophysical Research Abstracts. EGU General Assembly, p. 3.

Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaie, F., & Blaschke, T. (2019). Multi-hazard probability assessment and mapping in Iran. Science of the total environment, 692, 556-571.https://doi.org/10.1016/j.scitotenv.2019.07.203

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. https://doi.org/10.1007/s12040-013-0282-2

Pourghasemi, H. R., Kariminejad, N., Amiri, M., Edalat, M., Zarafshar, M., Blaschke, T., & Cerda, A. (2020). Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Scientific reports, 10(1), 1-11. https://doi.org/10.1038/s41598-020-60191-3

Pourghasemi, H. R., Moradi, H. R., & Aghda, S. F. (2013). Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural hazards, 69(1), 749-779.https://doi.org/10.1007/s11069-013-0728-5.

Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards, 63(2), 965-996. https://doi.org/10.1007/s11069-012-0217-2

Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., & Moezzi, K. D. (2012). Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In Terrigenous mass movements (pp. 23-49). Springer, Berlin, Heidelberg.https://doi.org/10.1007/978-3-642-25495-6_2

Pourghasemi, H. R., Pradhan, B., Gokceoglu, C., Mohammadi, M., & Moradi, H. R. (2013). Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences, 6(7), 2351-2365. https://doi.org/10.1007/s12517-012-0532-7

Pourghasemi, H.R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162, 177–192. https://doi.org/10.1016/j.catena.2017.11.022

Pradhan, A. M. S., & Kim, Y. T. (2016). Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena, 140, 125-139. https://doi.org/10.1016/j.catena.2016.01.022

Pradhan, B. (2011). Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Sciences, 63(2), 329-349. https://doi.org/10.1007/s12665-010-07051

Pradhan, B. (2013). 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.  https://doi.org/10.1016/j.cageo.2012.08.023

Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747-759. https://doi.org/10.1016/j.envsoft.2009.10.016

Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60-91. https://doi.org/10.1016/j.earscirev.2018.03.001

Roccati, A., Faccini, F., Luino, F., Ciampalini, A., & Turconi, L. (2019). Heavy rainfall triggering shallow landslides: A susceptibility assessment by a GIS-approach in a Ligurian Apennine Catchment (Italy). Water, 11(3), 605. https://doi.org/10.3390/w11030605

Saha, A. K., Gupta, R. P., Sarkar, I., Arora, M. K., & Csaplovics, E. (2005). An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides, 2(1), 61-69.https://doi.org/10.1007/s10346-004-0039-8

Shrestha, D. P., & Zinck, J. A. (2001). Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal). International Journal of Applied Earth Observation and Geoinformation, 3(1), 78-85. https://doi.org/10.1016/S0303-2434(01)85024-8

Song, S., Zhan, Z., Long, Z., Zhang, J., & Yao, L. (2011). Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data. PloS one, 6(2), e17191. https://doi.org/10.1371/journal.pone.0017191

Suzen, M. L., & Toprak, V. E. D. A. T. (1998). Filtering of satellite images in geological lineament analyses: an application to a fault zone in Central Turkey. International journal of remote sensing, 19(6), 1101-1114. https://doi.org/10.1080/014311698215621

Talebi, A., Troch, P. A. A., & Uijlenhoet, R. (2006). A steady-state analytical hillslope stability model. https://doi.org/10.1002/hyp.6881

Talebi, A., Uijlenhoet, R., & Troch, P. A. (2008). A low‐dimensional physically based model of hydrologic control of shallow landsliding on complex hillslopes. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 33(13), 1964-1976. https://doi.org/10.1002/esp.1648

Talebi, A., Uijlenhoet, R., & Troch, P. A. (2008). Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows. Natural Hazards and Earth System Sciences, 8(4), 733-744.https://doi.org/10.5194/nhess-8-733-2008

Tangestani, M. H., & Moore, F. (2001). Porphyry copper potential mapping using the weights of evidence model in a GIS, northern Shahr e Babak, Iran. Australian Journal of Earth Sciences, 48(5), 695-701.https://doi.org/10.1046/j.1440-0952.2001.485889.x

Tseng, C. M., Lin, C. W., & Hsieh, W. D. (2015). Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories. Natural Hazards and Earth System Sciences Discussions, 3(2), 1137-1173. https://doi.org/10.5194/nhessd-3-1137-2015

Van Beek, L. P. H., & Van Asch, T. W. (2004). Regional assessment of the effects of land-use change on landslide hazard by means of physically based modelling. Natural Hazards, 31(1), 289-304. https://doi.org/10.1023/B:NHAZ.0000020267.39691.39

Van Beek, L.P.H., Wint, J., Cammeraat, L.H., & Edwards, J.P. (2005). Observation and simulation of root reinforcement on abandoned Mediterranean slopes: Plant and Soil. 278, p. 55-74. https://doi.org/10.1007/s11104-005-7247-4

Van Westen, C. J., & Alzate Bonilla, J. B. (1990). Mountain hazard analysis using a PC-based GIS. In International congress international association of engineering geology. 6 (pp. 265-271).

Van Westen, C. J., Van Asch, T. W., & Soeters, R. (2006). Landslide hazard and risk zonation—why is it still so difficult?. Bulletin of Engineering geology and the Environment, 65(2), 167-184.https://doi.org/10.1007/s10064-005-0023-0

Vapnik, V.N. (2001). The nature of statistical learning theory. Statistics for Engineering and Information Science, 2nd edn. Springer, New York. https://doi.org/10.1007/978-1-4757-3264-1

Vijith, H., & Madhu, G. (2008). Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environmental Geology, 55(7), 1397-1405.  https://doi.org/10.1007/s00254-007-1090-2

Wang, C. M., & Huang, Y. F. (2009). Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data. Expert systems with Applications, 36(3), 5900-5908.  https://doi.org/10.1016/j.eswa.2008.07.026

Wang, L. J., Sawada, K., & Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81-92. https://doi.org/10.1016/j.cageo.2013.04.006

Weier, J., & Herring, D. (2005). Measuring vegetation (NDVI and EVI). Earth observatory Library of NASA, http:// earthobservatory.nasa.gov/Library/MeasuringVegetation/.

Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.

Wu, W., & Sidle, R. C. (1995). A distributed slope stability model for steep forested basins. Water resources research, 31(8), 2097-2110. https://doi.org/10.1029/95WR01136

Xiao, T., Yin, K., Yao, T., & Liu, S. (2019). Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China. Acta Geochim. 38, 654–669. https://doi.org/10.1007/s11631-019-00341-1

Yalcin, A. (2005). An investigation on Ardesen (Rize) region on the basis of landslide susceptibility. Ph.D. Thesis, Karadeniz Technical University, Trabzon, Turkey (in Turkish).

Yan, F., Zhang, Q., Ye, S., & Ren, B. (2019). A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology, 327, 170-187. https://doi.org/10.1016/j.geomorph.2018.10.024

Yao, X., Zhang, Y.S., Wang, X.L., & Xiong, T.Y. (2008). Automatic hierarchical approach to detecting shallow landslides and avalanches by the combination of multi-spectral RS imagery and DEM derivatives. Geol Bull China 27:1870–1874.

Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6), 1125-1138. https://doi.org/10.1016/j.cageo.2008.08.007

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 Sciences, 61(4), 821-836. https://doi.org/10.1007/s12665-009-0394-9

Zhao, S., & Zhao, Z. A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/8854606

Zhu, X., Zhang, S., Jin, Z., Zhang, Z., & Xu, Z. (2010). Missing value estimation for mixed-attribute data sets. IEEE Transactions on Knowledge and Data Engineering, 23(1), 110-121. https://doi.org/10.1109/TKDE.2010.99