Semiannual, Islamic Azad University (Meybod Branch)

Document Type : Research Article


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


Background and objective: The SVM algorithm is an applied method that has been considered in recent years to study landslides. The main purpose of this study is to evaluate the mapping power of the GIS-based SVM model with kernel functions analysis for spatial prediction of landslides at the Ilam dam watershed.
Materials and methods: According to review sources, 14 underlying factors including elevation, slope, aspect, plan curvature, profile curvature, LS factor, TWI, SPI, Lithologic units, land cover, NDVI, road distance, distance to the drainage channel, distance to fault were selected as factors affecting the occurrence of landslides in the study area and the mentioned layers were prepared in the GIS. In the present study, the non-linear two-class SVM method was used, the two-class SVM requires both datasets representing the occurrence of landslides and non-occurrence of landslides. The landslide inventory was randomly divided into a training dataset of 75% for building the models and the remaining 25% for the validation of the models.
Results and conclusion: The validation results showed that the area of the prediction-rate curve under the curve (AUC) for landslide susceptibility maps produced by the SVM linear function, SVM polynomial function, SVM radial basic function, and SVM sigmoid function are 0.946, 0.931, 0.912, and 0.871 respectively. To assess the influences of factors on the landslide susceptibility map were used the Cohen’s kappa index of the model. The result shows that the most effective factors are the distance to roads, distance to drainages, and plan curvature in this area.


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. Geomorphology318, 101-111.
Akgun, A., & Türk, N. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environmental Earth Sciences61(3), 595-611.
Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences44, 120 135.
Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical geosciences44(1), 47 70.
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 hazards64(1), 547-575.
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 Journal24(1), 43-69.
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 Sciences5(6), 853-862.
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 Landforms41(5), 711-720.
Budimir, M. E. A., Atkinson, P. M., & Lewis, H. G. (2015). A systematic review of landslide probability mapping using logistic regression. Landslides12(3), 419-436.
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.
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.
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.
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. Landslides13(2), 361-378.
Burrough, P. A. (1986). Principles of geographical. Information systems for land resource assessment. Clarendon Press, Oxford.
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 landforms16(5), 427 445.
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. Geomorphology297, 69-85.
Chung, C. J. F., & Fabbri, A. G. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric engineering and remote sensing65(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.
Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2002). A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology48(4), 349-364.
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 Sciences118, 53-64.
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 learning20(3), 273 297.
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 Geology54(2), 311-324.
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 Geology40(3), 381-391.
Damaševičius, R. (2010). Optimization of SVM parameters for recognition of regulatory DNA sequences. Top18(2), 339-353.
DeGraff, J. V. (1979). Initiation of shallow mass movement by vegetative-type conversion. Geology7(9), 426-429.;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 hazards65(1), 135-165.
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 Hazards32(1), 1-23.
Ercanoglu, M., & Gokceoglu, C. (2003). Landslide Susceptibility Zoning of North Yenice (NW Turkey) by Multivariate Statistical Techniques. Natural Hazard 00(00):1-23.
Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology66(1-4), 327-343.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters27(8), 861-874.
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 Sciences133, 15-24.
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 Sensing11(2), 196.
Goetz, J. N., Guthrie, R. H., & Brenning, A. (2011). Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology129(3-4), 376-386.
Gordo, C., Zêzere, J. L., & Marques, R. (2019). Landslide susceptibility assessment at the basin scale for rainfall-and earthquake-triggered shallow slides. Geosciences9(6), 268.
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.
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. Geomorphology37(1-2), 149 165.
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. Geomorphology224, 69-85.
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. Geomorphology31(1-4), 181-216.
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology81(1-2), 166 184.
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. Geosciences8(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 Environment51(1), 138-156.
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-Information8(6), 266.
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 Group29(5), 599-610.
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). Catena163, 399-413.
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.
Huang, Y., & Zhao, L. (2018). Review on landslide susceptibility mapping using support vector machines. Catena165, 520-529.
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 Risk9(1), 49-69.
Kanevski, M., Pozdnoukhov, A., & Timonin, V. (2009). Machine learning for spatial environmental data: theory, applications, and software. EPFL Press, Lausanne, p pp 275.
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 Geology85(3-4), 347-366.
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 computation15(7), 1667-1689.
Kornejady, A., Ownegh, M., & Bahremand, A. (2017). Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena152, 144-162.
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. Catena79(1), 27-38.
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 landforms34(6), 867-881.
Lacasse, S., & Nadim, F. (2009). Landslide risk assessment and mitigation strategy. In Landslides–disaster risk reduction (pp. 31-61). Springer, Berlin, Heidelberg.
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 geology40(9), 1095-1113.
Lee, S., Chwae, U., & Min, K. (2002). Landslide susceptibility mapping by correlation between topography and geological structure: the Janghung area, Korea. Geomorphology46(3-4), 149-162.
Lee, S., Hong, S. M., & Jung, H. S. (2017). A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability9(1), 48.
Lee, S., Hwang, J., & Park, I. (2013). Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. Catena100, 15-30.
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.
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 Geology97(1-2), 32 41.
Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology123(3), 225-234.
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 Sciences61, 221-236.
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.
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.
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.
Pachauri, A. K., Gupta, P. V., & Chander, R. (1998). Landslide zoning in a part of the Garhwal Himalayas. Environmental Geology, 36(3), 325-334.
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.
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.
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.
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.
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.
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.
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.
Pourghasemi, H.R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: which algorithm, which precision? Catena 162, 177–192.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Talebi, A., Troch, P. A. A., & Uijlenhoet, R. (2006). A steady-state analytical hillslope stability model.
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.
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.
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.
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.
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.
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.
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.
Vapnik, V.N. (2001). The nature of statistical learning theory. Statistics for Engineering and Information Science, 2nd edn. Springer, New York.
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.
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.
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.
Weier, J., & Herring, D. (2005). Measuring vegetation (NDVI and EVI). Earth observatory Library of NASA, http://
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.
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.
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.
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.
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.
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.
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.