Large-scale Mapping of Landslide and Debris Flow using Flowr Model with Statistical and Machine Learning Methods
Main Article Content
Abstract
The main purpose of this article is to establish a susceptibility zonation map of the landslides and debris flows in Phin Ngan commune, Bat Xat district, Lao Cai province on a large scale using statistical methods and machine learning combined with the FlowR model. First, the five Landslide Susceptibility Index (LSI) maps were established from two statistical models (Logistic Regression - LR, Discriminant Analysis – DA) and three machine learning models (Bayesian Network – BN, Artificial Neural Network – ANN, Support Vector Machine – SVM) were generated based on seven maps of landslide conditioning factors (slope, curvature, stream power index-SPI, topographic wetness index-TWI, sediment transportation index-STI, land use/land cover and weathering crust). Next, the five LSI maps will be evaluated for performance with the value of Area Under the Curve (AUC) according to the Receiver Operating Characteristic (ROC) curve. After that, a susceptibility map of debris flow established with FlowR software was combined with the five LSI maps created from five statistical and machine learning methods to generate a susceptibility zonation map of landslides and debris flows in the study area. The area percentage of the locations with landslides and debris flows located in the zones of susceptibility (very low, low, medium, high, very high), which were created from five combined methods: BN-FlowR, LR-FlowR, DA-FlowR, ANN-FlowR, and SVM-FlowR, were compared and evaluated. The results indicate that the integrated models have given outputs with good forecasting ability. They are also very useful in land-use planning as well as the prevention and mitigation of risks due to landslides and debris flows in the research area and other similar mountainous areas.
Keywords:
Landslide, debris flow, FlowR, statistical model, machine learning model.
References
[1] E. Leroi, Landslide Hazard-risk Maps at Different Scales: Objectives, Tools and Developments, in: Proc VII Int Symp Landslides, Trondheim, June 1996, pp. 35-52,
https://doi.org/10.12691/jgg-5-4-2.
[2] P. Aleotti, R. Chowdhury, Landslide Hazard Assessment: Summary Review and New Perspectives, Bull Eng Geol Environ, Vol. 58,
No. 1, 1999, pp. 21-44, https://doi.org/10.1007/s100640050066.
[3] J. Corominas, C. V. Westen, P. Frattini, L. Cascini, J. P. Malet, S. Fotopoulou, F. Catani, M. V. D. Eeckhaut, O. Mavrouli, F. Agliardi, K. Pitilakis,
M. G. Winter, M. Pastor, S. Ferlisi, V. Tofani,
J. Herv, J. T. Smith, Recommendations for the Quantitative Analysis of Landslide Risk, Bull. Eng. Geol, Environ, Vol. 73, 2014, pp. 209-263, https://doi.org/10.1007/s10064-013-0538-8.
[4] C. J. V. Westen, M. T. J. Terlien, An Approach Towards Deterministic Landslide Hazard Analysis in GIS, A Case Study from Manizales (Colombia), Earth Surf Process Landforms, Vol. 21, No. 9, 1996, pp. 853-868,
https://doi.org/10.1002/(SICI)1096-9837(199609) 21:9<853::AID-ESP676>3.0.CO;2-C.
[5] J. N. Goetz, A. Brenning, H. Petschko, P. Leopold, Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling, Computers and Geosciences, Vol. 81, 2015, pp. 1-11, https://doi.org/10.1016/j.cageo.2015.04.007.
[6] J. L. Zezere, S. Pereira, R. Melo, S. C. Oliveira,
R. A. C. Garcia, Mapping Landslide Susceptibility Using Data-driven Methods, Science of Total Environment, Vol. 589, 2017, pp. 250-267, https://doi.org/ 10.1016/j.scitotenv.2017.02.188.
[7] Y. Huang, L. Zhao, Review on Landslide Susceptibility Mapping Using Support Vector Machines, Catena, Vol. 165, 2018, pp. 520-529, https://doi.org/10.1016/j.catena.2018.03.003.
[8] S. Lee, K. Min, Statistical Analysis of Landslide Susceptibility at Yongin, Korean, Environmental Geology, Vol. 40, 2001, pp. 1095-1113, https://doi.org/10.1007/s002540100310.
[9] S. Lee, T. Sambath, Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Using Frequency Ratio and Logistic Regression Models, Environ Geol, Vol. 50, 2006, pp. 847-855, https://doi.org/10.1007/s00254-006-0256-7.
[10] S. Lee, B. Pradhan, Landslide Hazard Mapping at Selangor, Malaysia Using 42 Frequency Ratio and Logistic Regression Models, Landslides, Vol. 4, 2007, pp. 33-41, https://doi.org/10.1007/s10346-006-0047-y.
[11] A. Yalcin, S. Reis, A. C, Aydinoglu,
T. Yomralioglu, A GIS-based Comparative Study of Frequency Ratio, Analytical Hierarchy Process, Bivariate Statistics and Logistics Regression Methods for Landslide Susceptibility Mapping in Trabzon, NE Turkey, Catena, Vol. 85, No. 3, 2011, pp. 274-287, http://dx.doi.org/10.1016/j.catena.2011.01.014.
[12] M. Mohammady, H. R. Pourghasemi, B. Pradhan, Landslide Susceptibility Mapping At Golestan Province, Iran: A Comparison Between Frequency Ratio, Dempster-Shafer, and Weights of Evidence Models, J. Asian Earth Sci, Vol. 61, 2012,
pp. 221-236, http://dx.doi.org/10.1016/j.jseaes.2012.10.005.
[13] A. D. Regmi, K. C. Devkota, K. Yoshida,
B. Pradhan, H. R. Pourghasemi, T. Kumamoto,
A. Akgun, Application of Frequency Ratio, Statistical Index, and Weights of Evidence Models and Their Comparison in Landslide Susceptibility Mapping in Central Nepal Himalaya, Arab
J. Geosci, Vol. 7, No. 2, 2014, pp. 725-742, http://dx.doi.org/10.1007/s12517-012-0807-z.
[14] R. K. Dahal, S. Hasegawa, A. Nonomura,
M. Yamanaka, S. Dhakal, P. Paudyal, Predictive Modeling of Rainfall-Induced Landslide Hazard In The Lesser Himalaya of Nepal Based on Weights of Evidence, Geomorphology, Vol. 102, No. 3-4, 2008, pp. 496-510, http://dx.doi.org/10.1016/j.geomorph.2008.05.041.
[15] R. K. Dahal, S. Hasegawa, A. Nonomura,
M. Yamanaka, T. Masuda, K. Nishino, GIS-based Weights of Evidence Modeling of Rainfall-Induced Landslides in Small Catchments For Landslide Susceptibility Mapping, Environ Geol, Vol. 54, No. 2, 2008, pp. 311-324, http://dx.doi.org/10.1007/s00254-007-0818-3.
[16] N. R. Regmi, J. R. Giardino, J. D. Vitek, Modeling Susceptibility to Landslides Using The Weight of Evidence Approach: Westen Colorado, USA, Geomorphology, Vol. 115, No. 1-2, 2010,
pp. 172-187, http://dx.doi.org/10.1016/j.geomorph.2009.10.002.
[17] H. J. Oh, S. Lee, Assessment of Ground Subsidence Using GIS and the Weights of Evidence Model, Eng Geol, Vol. 115, No. 1-2, 2010, pp. 36-48, http://dx.doi.org/10.1016/j.enggeo.2010.06.015.
[18] C. Xu, X. Xu, F. C. Dai, J. Z. Xiao, X. B. Tan,
R. M. Yuan, Landslide Hazard Mapping Using GIS and Weight of Evidence Model in Qingshui River Watershed of 2008 Wenchuan Earthquake Struck Region, J. Earth Sci, Vol. 23, No. 1, 2012,
pp. 97-120, http://dx.doi.org/10.1007/s12583-012-0236-7.
[19] H. R. Pourghasemi, B. Pradhan, C. Gokceoglu,
M. Mohammadi, H. R. Moradi, Application of Weights of Evidence and Certainty Factor Models and Their Comparison In Landslide Susceptibility Mapping At Haraz Watershed, Iran, Arab J. Geosci, Vol. 6, No. 7, 2013, pp. 2351-2365, http://dx.doi.org/10.1007/s12517-012-0532-7.
[20] E. R. Sujatha, P. Kumaravel, G. V. Rajamanickam, Assessing Landslide Susceptibility Using Bayesian Probability-Based Weight of Evidence Model, Bull Eng Geol Environ, Vol. 73, No. 1, 2014,
pp. 147-161,
http://dx.doi.org/10.1007/s10064-013-0537-9.
[21] Q. Q. Wang, W. P. Li, M. L. Xing, Y. L. Wu, Y. B. Pei, D. D. Yang, H. Y. Bai, Landslide Susceptibility Mapping At Gongliu County, China Using Artificial Neural Network and Weight of Evidence Models, Geosci J, Vol. 20, No. 5, 2016, pp. 705-718,
http://dx.doi.org/10.1007/s12303-016-0003-3.
[22] M. D. Hien, K. L. Yin, Z. Z. Guo, A Comparative Study on the Integrative Ability of the Analytical Hierarchy Process, Weights of Evidence And Logistic Regression Methods with the Flow-R Model for Landslide Susceptibility Assessment, Geomatics, Natural Hazard and Risk, Vol. 11,
No. 1, pp. 2449-2485, https://doi.org/10.1080/19475705.2020.1846086.
[23] T. Chen, R. Q. Niu, X. P. Jia, A Comparison of Information Value and Logistic Regression Models In Landslide Susceptibility Mapping By Using GIS, Environ Earth Sci, Vol. 75, No. 10, 2016,
pp. 867,
http://dx.doi.org/10.1007/s12665-016-5317-y.
[24] F. Mengistu, K. V. Suryabhagavan, T. K. Raghuvanshi, E. Lewi, Landslide Hazard Zonation and Slope Instability Assessment Using Optical and Insar Data: A Case Study from Gidole Town and Its Surrounding Areas, Southern Ethiopia, Remote Sensing of Land, Vol. 3, No. 1, 2019,
pp. 1-14, https://doi.org/10.21523/gcj1.19030101.
[25] G. F. Zhang, Y. X. Cai, Z. Zheng, J. W. Zhen,
Y. L. Liu, K. Y. Huang, Integration of the Statistical Index Method and the Analytic Hierarchy Process Technique for the Assessment of Landslide Susceptibility in Huizhou, China, Catena, Vol. 142, 2016, pp. 233-244, http://dx.doi.org/10.1016/j.catena.2016.03.028.
[26] Z. Wu, Y. Wu, Y. Yang, F. Chen, N. Zhang, Y. Ke, W. Li, A Comparative Study on the Landslide Susceptibility Mapping Using Logistic Regression And Statistical Index Models, Arab J. Geosci,
Vol. 10, No. 8, 2017, pp. 1-17, http://dx.doi.org/10.1007/s12517-017-2961-9.
[27] H. J. Oh, S. Lee, G. M. Soedradjat, Quantitative Landslide Susceptibility Mapping at Pemalang Area, Indonesia, Environmental Earth Sciences, Vol. 60, No. 6, 2010, pp. 1317-1328, https://doi.org/10.1007/s12665-009-0272-5.
[28] B. Pradhan, Manifestation of an Advanced Fuzzy Logic Model Coupled With Geo-Information Techniques to Landslide Susceptibility Mapping and Their Comparison with Logistic Regression Modelling, Environ Ecol Stat, Vol. 18, No. 3, 2011, pp. 471-493,
http://dx.doi.org/10.1007/s10651-010-0147-7.
[29] B. Pradhan, 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, Vol. 51, 2013, pp. 350-365, http://dx.doi.org/10.1016/J.Cageo.2012.08.023.
[30] H. R. Pourghasemi, A. G. Jirandeh, B. Pradhan,
C. Xu, C. Gokceoglu, Landslide Susceptibility Mapping Using Support Vector Machine and GIS at the Golestan Province, J Earth Syst Sci,
Vol. 122, No. 2, 2013, pp. 349-369, https://doi.org/10.1007/S12040-013-0282-2.
[31] H. Y. Hong, B. Pradhan, C. Xu, T. B. Dieu, Spatial Prediction of Landslide Hazard at the Yihuang Area (China) Using Two-Class Kernel Logistic Regression, Alternating Decision Tree and Support Vector Machines, Catena, Vol. 133, 2015,
pp. 266-281, http://dx.doi.org/10.1016/j.catena.2015.05.019.
[32] B. T. Dieu, H. Shahabi, A. Shirzadi, K. Chapi,
B. Pradhan, W. Chen, K. Khosravi, M. Panahi,
B. B. Ahmad, L. Saro, Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms, Sensors, Vol. 18, No. 8, 2018, pp. 1-20, https://doi.org/10.3390/S1808246.
[33] L. Ermini, F. Catani, N. Casagli, Artificial Neural Networks Applied to Landslide Susceptibility Assessment, Geomorphology, Vol. 66, 2005,
pp. 327-343, https://doi.org/10.1016/j.geomorph.2004.09.025.
[34] H. A. Nefeslioglu, C. Gokceoglu, H. Sonmez, An Assessment on the Use of Logistic Regression and Artificial Neural Networks with Different Sampling Strategies for the Preparation of Landslide Susceptibility Maps, Eng Geol, Vol. 97, No. 3-4, 2008, pp. 171-91, https://doi.org/10.1016/j.enggeo.2008.01.004.
[35] B. Pradhan, S. Lee, Landslide Risk Analysis Using Artificial Neural Network Model Focussing on Different Training Sites, Int J Physsci, Vol. 4, 2009, pp. 001-015, https://doi.org/10.5897/ijps.9000343.
[36] B. Pradhan, S. Lee, Delineation Of Landslide Hazard Areas on Penang Island, Malaysia, By Using Frequency Ratio, Logistic Regression, and Artificial Neural Network Models, Environmental Earth Sciences, Vol. 60, No. 5, 2010, pp. 1037-1054, https://doi.org/10.1007/s12665-009-0245-8.
[37] B. Pradhan, S. Lee, Landslide Susceptibility Assessment and Factor Effect Analysis: Backpropagation Artificial Neural Networks and Their Comparison with Frequency Ratio and Bivariate Logistic Regression Modelling, Environ ModellSoftw, Vol. 25, 2010, pp. 747-59, https://doi.org/10.1016/j.envsoft.2009.10.016.
[38] I. Yilmaz, Comparison of Landslide Susceptibility Mapping Methodologies For Koyulhisar, Turkey: Conditional Probability, Logistic Regression, Artificial Neural Networks, and support vector machine, Environmental Earth Sciences, Vol. 61, No. 4, 2010, pp. 821-836, https://doi.org/10.1007/s12665-009-0394-9.
[39] P. T. Binh, B. Pradhan, T. D. Bui, I. Prakash, M. B. Dholakia, A Comparative Study of Different Machine Learning Methods for Landslide Susceptibility Assessment: A Case Study of Uttarakhand Area (India), Environmental Modelling & Software, Vol. 84, 2016, pp. 240-250, https://doi.org/10.1016/j.envsoft.2016.07.005.
[40] J. Corominas, The Angle of Reach as Amobility Index For Small And Large Landslides, Canadian Geotechnical Journal, Vol. 33, No. 2, 1996,
pp. 260–271, https://doi.org/10.1139/t96-005.
[41] F. Legros, Themobility of Long-Runout Landslides, Engineering Geology, Vol. 63, 2002, pp. 301-331,
https://doi.org/10.1016/S0013-7952(01)00090-4.
[42] R. M. Iverson, S. P. Schilling, J. W. Vallance, Objective Delineation of Lahar-Inundation Hazard Zones, Geological Society of America Bulletin, Vol. 110, No. 8, 1998, pp. 972-984, https://doi.org/10.1130/0016-7606(1998)110<0972:ODOLIH>2.3.CO;2.
[43] G. B. Crosta, S. Cucchiaro, P. Frattini, Validation of Semi-Empirical Relationships for the Definition of Debris-flow Behavior In Granular Materials, in: Rickenmann, D., Chen, C. (Eds.), 3rd Int. Conf. on Debris-Flow Hazards Mitigation, Millpress, Davos, 2003, pp. 821-831.
[44] R. M. Iverson, The Physics of Debris Fows, Review of Geophysics, Vol. 35, No. 3, 1997,
pp. 245-296, https://doi.org/10.1029/97RG00426.
[45] M. Hürlimann, D. Rickenmann, V. Medina, A Bateman, Evaluation of Approaches to Calculate Debris-Flow Parameters for Hazard Assessment, Engineering Geology, Vol. 102, 2008, pp. 152-163, https://doi.org/10.1016/j.enggeo.2008.03.012.
[46] D. Rickenmann,D. Laigle, B. McArdell, J. Hübl, Comparison of 2D Debris-flow Simulation Models with field Events, Computational Geosciences, Vol. 10, No. 2, 2006, pp. 24-264, https://doi.org/10.1007/s10596-005-9021-3.
[47] K. Schraml, B. Thomschitz, B. W. McArdell,
C. Graf, R. Kaitna, Modeling Debris-flow Runout Patterns on Two Alpine Fans With Different Dynamic Simulation Models, Nat Hazards Earth Syst Sci, Vol. 15, 2015, pp. 1483-1492, https://doi.org/10.5194/nhess-15-1483-2015.
[48] J. Blahut, P. Horton, S. Sterlacchini,
M. Jaboyedoff, Debris Fow Hazard Modelling on Medium Scale: Valtellina di Tirano, Italy, Nat. Hazards Earth Syst Sci, Vol. 10, 2010,
pp. 2379-2390, https://doi.org/10.5194/nhess-10-2379-2010.
[49] P. Horton, M. Jaboyedoff, M. Zimmermann,
B. Mazotti, C. Longchamp, Flow-R, a Model for Debris Flow Susceptibility Mapping at A Regional Scale – Some Case Studies, 5th Int Conf on Debris-Flow Hazards Mitigation, Padua, Italy, Italian Journal of Engineering Geology and Environment, 2011, pp. 875-884.
[50] P. Horton, M. Jaboyedoff, B. Rudaz, M. Zimmermann, Flow-R, a Model for Susceptibility Mapping of Debris Flows and Other Gravitational Hazards At A Regional Scale, Nat Hazards Earth Syst Sci, Vol. 13, No. 4, 2013, pp. 869-885, http://dx.doi.org/10.5194/nhess-13-869-2013.
[51] M. Jaboyedoff, Ch. Choffet, M. H. Derron,
P. Horton, A. Loye, C. Longchamp, B. Mazotti,
C. Michoud, A. Pedrazzini, Preliminary Slope Mass Movements Susceptibility Mapping Using DEM and LiDAR DEM, in: Terrigenous Mass Movements: Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology, Edited by: Pradhan, B. And Buchroithner, M., Springer-Verlag, Berlin Heidelberg, Germany, 2012, pp. 109-170, https://doi.org/10.1007/978-3-642-25495-6_5.
[52] B. Q. Luna, J. Blahut, M. Kappes, S. O. Akbas,
J. P. Malet, A. Remaitre, T. V. Asch, M. Jaboyedoff, Chapter 5: Methods for Debris Flow Hazard and Risk Assessment, in Van Asch T Ed, Mountain Risks: From Prediction To Management and Governance, Advances in Natural and Technological Hazards Research, Vol. 34, 2014, http://dx.doi.org/10.1007/978-94-007-6769-05.
[53] B. Q. Luna, J. Blahut, C. Camera, C. Van Westen, T. Apuani, V. Jetten, S. Sterlacchini, Physically Based Dynamic Run-Out Modelling for Quantitative Debris Flow Risk Assessment: A Case Study in Tresenda, Northern Italy, Environ Earth Sci, Vol. 72, 2014, pp. 645-661, http://dx.doi.org/10. 1007/s12665-013-2986-7.
[54] B. Q. Luna, J. Blahut, T. V. Asch, C. V. Westen, M. Kappes, ASCHFLOW: A Dynamic Landslide Run-Out Model for Medium Scale Hazard Analysis, Geoenviron Disasters, Vol. 3, 2016,
pp. 1-17,
http:// dx.doi.org/10.1186/s40677-016-0064-7.
[55] S. H. Kang, S. R. Lee, N. Nikhil, J. Y. V. Park,
D. H. Lee, Development of An Initiation Criterion for Debris Flows Based on Local Topographic Properties and Applicability Assessment at A Regional Scale, Eng Geol, Vol. 230, 2017, pp. 64-76, http://dx.doi.org/10.1016/j.enggeo.2017.09.017.
[56] S. Kang, S. R. Lee, Debris Flow Susceptibility Assessment Based on An Empirical Approach in The Central Region of South Korea, Geomorphology, Vol. 308, 2018, pp. 1-12, https://doi.org/10.1016/j.geomorph.2018.01.025.
[57] N. T. Long, F. D. Smedt, Application of an Analytical Hierarchical Process Approach for Landslide Susceptibility Mapping in A Luoi District, Thua Thien Hue Province Vietnam, Environ Earth Sci, Vol. 66, 2012, pp. 1739-1752, http://dx.doi.org/10.1007/s12665-011-1397-x.
[58] L. Q. Hung, N. T. H. Van, M. D. Duc, L. T. C. Ha, P. V. Son, N. H. Khanh, L. T. Binh, Landslide Susceptibility Mapping by Combining the Analytical Hierarchy Process and Weighted Linear Combination Methods: A Case Study in the Upper Lo River Catchment (Vietnam), Landslides,
Vol. 13, 2016, pp. 1285-1301,
https://doi.org/ 10.1007/s10346-015-0657-3.
[59] N. T. Long, F. D. Smedt, Analysis and Mapping of Rainfall-Induced Landslide Susceptibility in A Luoi District, Thua Thien Hue Province, Vietnam, Water-MDPI, Vol. 11, 2019, https://doi.org/10.3390/w11010051.
[60] B. T. Dieu, B. Pradhan, O. Lofman, I. Revhaug,
O. B. Dick, Spatial Prediction of Landslide Hazards in Hoabinh Province (Vietnam): A Comparative Assessment of the Efficacy of Evidential Belief Functions And Fuzzy Logic Models, Catena, Vol. 96, 2012, pp. 28-40, https://doi.org/10.1016/j.catena.2012.04.001.
[61] B. T. Dieu, B. Pradhan, O. Lofman, I. Revhaug,
O. B. Dick, Landslide Susceptibility Assessment in The Hoabinh Province of Vietnam: A Comparison of the Levenberg-Marquardt and Bayesian Regularized Neural Networks, Geomorphology, Vol. 171-172, 2012, pp. 12-29, https://doi.org/10.1016/j.geomorph.2012.04.023.
[62] B. T. Dieu, P. T. Binh, Q. P. Nguyen, N. D. Hoang, Spatial Prediction of Rainfall-Induced Shallow Landslides Using Hybrid Integration Approach of Least-Squares, Support Vector Machines And Differential Evolution Optimization: A Case Study In Central Vietnam, Int J Digital Earth, 2016,
pp. 1-21, https://doi.org/10.1080/17538947.2016.1169561.
[63] P. T. Binh, B. Pradhan, B. T. Dieu, Spatial Prediction of Landslides Using A Hybrid Machine Learning Approach Based on Random Subspace and Classification and Regression Trees, Geomorphology, Vol. 303, 2018, pp. 256-270, https://doi.org/10.1016/j.geomorph.2017.12.008.
[64] P. T. Binh, N. T. Thoi, C. C. Qi, T. V. Phong,
J. Dou, H. S. Lanh, L. V. Hiep, I. Prakash., Coupling RBF Neural Network with Ensemble Learning Techniques for Landslide Susceptibility Mapping, Catena, Vol. 195, https://doi.org/10.1016/j.catena.2020.104805.
[65] Vietnam Meteorological and Hydrological Administration, http://vmha.gov.vn/public/kttv-voi-san-xuat-va-doi-song-106/nhieu-hiem-nguy-thien-tai-rinh-rap-nguoi-dan-vung-cao-lao-cai-12248.html (accessed on: May 9th, 2022).
[66] Laocai Radio and Television, http://laocaitv.vn/lu-quet/sat-lo-dat-khien-2-nguoi-tu-vong-o-xa-phin-ngan-huyen-bat-xat (accessed on: May 9th 2022).
[67] N. X. Khien et al., Assess the Current Status of Geological Hazards in Ha Giang, Cao Bang, Tuyen Quang and Bac Kan Provinces, Identify Causes, Forecast and Propose Measures to Prevent and Minimize Consequences, Vietnam Institute of Geosciences and Mineral Resources, 2011
(in Vietnamese).
[68] O. F. Althuwaynee, B. Pradha, H. J. Park, J. H. Lee, A Novel Ensemble Bivariate Statistical Evidential Belief Function with Knowledge-Based Analytical Hierarchy Process and Multivariate Statistical Logistic Regression for Landslide Susceptibility Mapping, Catena, Vol. 114, 2014, pp. 21-36,
http:// dx.doi.org/10.1016/j.catena.2013.10.011.
[69] E. Yesilnacar, T. Topal, Landslide Susceptibility Mapping: A Comparison of Logistic Regression and Neural Networks Methods In A Medium Scale Study, Hendek Region (Turkey), Eng Geol,
Vol. 79, No. 3-4, 2005, pp. 251-266, http://dx.doi.org/10.1016/j.enggeo.2005.02.002.
[70] H. Y. Hong, H. R. Pourghasemi, Z. S. Pourtaghi, Landslide Susceptibility Assessment in Lianhua County (China): A Comparison Between A Random Forest Data Mining Technique and Bivariate and Multivariate Statistical Models, Geomorphology, Vol. 259, 2016, pp. 105-118, http://dx.doi.org/10.1016/j.geomorph.2016.02.012.
[71] A. M. S. Pradhan, Y. T. Kim, Relative Effect Method of Landslide Susceptibility Zonation in Weathered Granite Soil: A Case Study in Deokjeok-Ri Creek, Nat Hazards, Vol. 72, 2014, pp. 1189-1217, http://dx. doi.org/10.1007/s11069-014-1065-z.
[72] A. M. S. Pradhan, Y. T. Kim, Spatial Data Analysis And Application of Evidential Belief Functions to Shallow Landslide Susceptibility Mapping At Mt, Umyeon, Seoul, Korea, Bull Eng Geol Environ, Vol. 76, No. 4, 2017, pp. 1263-1279, http://dx.doi.org/10.1007/s10064-016-0919-x.
[73] K. Beven, M. Kirkby, Un Modèle À Base Physique De Zone D'appel Variable De L'hydrologie Du Bassin Versant (A Physically Based, Variable Contributing Area Model of Basin Hydrology), Hydrol Sci J, Vol. 24, 1979, pp. 43-69.
[74] I. Yilmaz, Landslide Susceptibility Mapping Using Frequency Ratio, Logistic Regression, Artificial Neural Networks and Their Comparison: A Case Study from Kat Landslides (Tokat–Turkey), Comput Geosci, Vol. 35, 2009, pp. 1125-1138, https://doi.org/10.1016/j.cageo.2008.08.007.
[75] A. M. S. Pradhan, H. S. Kang, S. Lee, Y. T. Kim, Spatial Model Integration For Shallow Landslide Susceptibility and Its Runout Using A GIS-Based Approach in Yongin, Geocarto Int, 2016, https://doi.org/10. 1080/10106049.2016.1155658.
[76] I. D. Moore, G. J. Burch, Physical Basis of the Length–Slope Factor in the Universal Soil Loss Equation, Soil Sci Soc Am J, Vol. 50, No. 5, 1986, pp. 1294-1298, https://doi.org/10.2136/sssaj1986. 03615995005000050042x.
[77] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
[78] N. Friedman, D. Geiger, M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, Vol. 29, 1997, pp. 131-163, https://doi.org/10.1023/a:1007465528199.
[79] B. G. Marrcot, J. D. Steventon, G. D. Sutheland,
R. K. Mccann, Guidelines for Developing and Updating Bayesian Belief Networks Applied to Eological Modeling and Conservation, Can J for Res, Vol. 36, No. 12, 2006, pp. 3063-3074, https://doi.org/10.1139/x06-135.
[80] Y. Song, J. Gong, S. Gao, D. Wang, T. Cui, Y. Li, B. Wei, Susceptibility Assessment of Earthquake-Induced Landslides Using Bayesian Network: A Case Study in Beichuan China, Comput. Geosci, Vol. 42, 2012, pp. 189-199, https://doi.org/10.1016/j.cageo.2011.09.011.
[81] B. Pradhan, Manifestation of An Advanced Fuzzy Logic Model Coupled with Geo-Information Techniques to Landslide Susceptibility Mapping and Their Comparison with Logistic Regression Modelling, Environ Ecol Stat, Vol. 18, No. 3, 2011, pp. 471-493,
http://dx.doi.org/10.1007/s10651-010-0147-7.
[82] F. C. Dai, C. .F Lee, J. Li, Z. W.Xu, Assessment of Landslide Susceptibility on the Natural Terrain of Lantau Island, Hong Kong, Environ Geol, Vol. 40, 2001, pp. 381-391, https://doi.org/10.1007/S002540000163.
[83] C. T. Lee, C. C. Huang, J. F. Lee, K. L. Pan, M. N. Lin, J. J. Dong, Statistical Approach to Storm Event-Induced Landslides Susceptibility, Nat Hazard Earth Syst Sci, Vol. 8, No. 4, 2008, pp. 941-960, https://doi.org/10.5194/nhess-8-941-2008.
[84] W. R. Klecka, Discriminant Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, Sage Publications, Beverly Hills and London, 1980,
pp. 07-19.
[85] J. C. Davis, Statistics and Data Analysis in Geology, (3rd Ed), Johnwiley And Sons, New York, 2002.
[86] C. Liu, W. Y. Li, H. B. Wu, P. Lu, K. Sang, W. W. Sun, W. Chen, Y. Hong, R. X. Li, Susceptibility Evaluation and Mapping of China’s Landslides Based on Multi-Source Data, Nat. Hazards,
Vol. 69, 2013, pp. 1477-1495, https://doi.org/10.1007/S11069-013-0759-Y.
[87] S. Lee, J. H. Ryu, M. J. Lee, J. S. Won, The Application of Artificial Neural Networks to Landslide Susceptibility Mapping At Janghung, Korea, Mathematical Geology, Vol. 38, 2006, https://doi.org/10.1007/s11004-005-9012-x.
[88] A. H. Fath, F. Madanifar, M. Abbasi, Implementation of Multilayer Perceptron (MLP) And Radial Basis Function (RBF) Neural Networks to Predict Solution Gas-Oil Ratio of Crude Oil Systems, Petroleum, Vol. 6, 2020,
pp. 80-91, https://doi.org/10.1016/j.petlm.2018.12.002.
[89] V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd, Springer, New York, 1999.
[90] B. Schölkopf, A. Smola, Learning with Kernels: Support Vectormachines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, 2002.
[91] J. M. Moguerza, A. Munoz, Support Vector Machines With Applications, Stat Sci, Vol. 21, 2006, pp. 322-336,
https://doi.org 1214/088342306000000493.
[92] V. Cherkassky, F. Mulier, Learning From Data: Concepts, Theory, and Methods, Wiley, New York, 2007.
[93] T. Kavzoglu, E. K. Sahin, I. Colkesen, Landslide Susceptibility Mapping Using GIS-Basedmulti-Criteria Decision Analysis, Support Vector Machines, and Logistic Regression, Landslides, Vol. 11, No. 3, 2014, pp. 425-439, https://doi.org/10.1007/s10346-013-0391-7.
[94] J. A. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process Lett, Vol. 9, No. 3, 1999, pp. 293-300, https://doi.org/10.1023/a:1018628609742.
[95] B. Dixon, N. Candade, Multispectral Landuse Classification Using Neural Networks and Support Vector Machines: One or the Other, or Both?, Int J Remote Sens, Vol. 29, No. 4, 2008, pp. 1185-1206, https://doi.org/10.1080/01431160701294661.
[96] P. Kayastha, M. R. Dhital, F. D. Smedt
F, Application of the Analytical Hierarchy Process (AHP) for Landslide Susceptibility Mapping: A Case Study from the Tinau Watershed, West Nepal, Comput Geosci, Vol. 52, 2013,
pp. 398-408, http://dx.doi.org/10.1016/j.cageo.2012.11.003.
[97] C. J. F. Chung, A. G. Fabbri, Validation of Spatial Prediction Models for Landslide Hazard Mapping, Natural Hazards, Vol. 30, No. 3, 2003, pp. 451-472, https://doi.org/10.1023/b:nhaz.0000007172.62651.2b.
[98] H. R. Pourghasemi, H. R. Moradi, S. M. Fatemi Aghda, C. Gokceoglu, B. Pradhan, GIS-based Landslide Susceptibility Mapping with Probabilistic Likelihood Ratio and Spatial Multi-Criteria Evaluation Models (North of Tehran, Iran), Arab J Geosci, Vol. 7, No. 5, 2014, pp. 1857-1878, http://dx.doi.org/10.1007/s12517-012-0825-x.
[99] R. Talaei, Landslide Susceptibility Zonation Mapping Using Logistic Regression and Its Validation in Hashtchin Region, Northwest of Iran, Journal Geological Society of India, Vol. 84, 2014, pp. 68-86,
https://doi.org/10.1007/s12594-014-0111-5.
[100] J. W. Choi, H. J. Oh, H. J. Lee, C. W. Lee, S. Lee, Combining Landslide Susceptibility Maps Obtained from Frequency Ratio, Logistic Regression and Artificial Neural Network Models Using ASTER Images and GIS, Engineering Geology, Vol. 124, 2012, pp. 12-23, https://doi.org/10.1016/j.enggeo.2011.09.011.
[101] J. J. Dong, Y. Y. Tung, C. C. Chen, J. J. Liao,
Y. W. Pan, Discriminant Analysis of Theo Geomorphic Characteristics and Stability of Landslide Dams, Geomorphology, Vol. 110, 2009, pp. 162-171, https://doi.org/10.1016/j.geomorph.2009.04.004.
[102] S. W. He, P. Pan, L. Dai, H. J. Wang, J. P. Liu, Application of Kernel-Based Fisher Discriminant Analysis to Map Landslide Susceptibility In The Qinggan River Delta, Three Gorges, China, Geomorphology, Vol. 171-172, 2012, pp. 30-41, https://doi.org/10.1016/j.geomorph.2012.04.024.
https://doi.org/10.12691/jgg-5-4-2.
[2] P. Aleotti, R. Chowdhury, Landslide Hazard Assessment: Summary Review and New Perspectives, Bull Eng Geol Environ, Vol. 58,
No. 1, 1999, pp. 21-44, https://doi.org/10.1007/s100640050066.
[3] J. Corominas, C. V. Westen, P. Frattini, L. Cascini, J. P. Malet, S. Fotopoulou, F. Catani, M. V. D. Eeckhaut, O. Mavrouli, F. Agliardi, K. Pitilakis,
M. G. Winter, M. Pastor, S. Ferlisi, V. Tofani,
J. Herv, J. T. Smith, Recommendations for the Quantitative Analysis of Landslide Risk, Bull. Eng. Geol, Environ, Vol. 73, 2014, pp. 209-263, https://doi.org/10.1007/s10064-013-0538-8.
[4] C. J. V. Westen, M. T. J. Terlien, An Approach Towards Deterministic Landslide Hazard Analysis in GIS, A Case Study from Manizales (Colombia), Earth Surf Process Landforms, Vol. 21, No. 9, 1996, pp. 853-868,
https://doi.org/10.1002/(SICI)1096-9837(199609) 21:9<853::AID-ESP676>3.0.CO;2-C.
[5] J. N. Goetz, A. Brenning, H. Petschko, P. Leopold, Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling, Computers and Geosciences, Vol. 81, 2015, pp. 1-11, https://doi.org/10.1016/j.cageo.2015.04.007.
[6] J. L. Zezere, S. Pereira, R. Melo, S. C. Oliveira,
R. A. C. Garcia, Mapping Landslide Susceptibility Using Data-driven Methods, Science of Total Environment, Vol. 589, 2017, pp. 250-267, https://doi.org/ 10.1016/j.scitotenv.2017.02.188.
[7] Y. Huang, L. Zhao, Review on Landslide Susceptibility Mapping Using Support Vector Machines, Catena, Vol. 165, 2018, pp. 520-529, https://doi.org/10.1016/j.catena.2018.03.003.
[8] S. Lee, K. Min, Statistical Analysis of Landslide Susceptibility at Yongin, Korean, Environmental Geology, Vol. 40, 2001, pp. 1095-1113, https://doi.org/10.1007/s002540100310.
[9] S. Lee, T. Sambath, Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Using Frequency Ratio and Logistic Regression Models, Environ Geol, Vol. 50, 2006, pp. 847-855, https://doi.org/10.1007/s00254-006-0256-7.
[10] S. Lee, B. Pradhan, Landslide Hazard Mapping at Selangor, Malaysia Using 42 Frequency Ratio and Logistic Regression Models, Landslides, Vol. 4, 2007, pp. 33-41, https://doi.org/10.1007/s10346-006-0047-y.
[11] A. Yalcin, S. Reis, A. C, Aydinoglu,
T. Yomralioglu, A GIS-based Comparative Study of Frequency Ratio, Analytical Hierarchy Process, Bivariate Statistics and Logistics Regression Methods for Landslide Susceptibility Mapping in Trabzon, NE Turkey, Catena, Vol. 85, No. 3, 2011, pp. 274-287, http://dx.doi.org/10.1016/j.catena.2011.01.014.
[12] M. Mohammady, H. R. Pourghasemi, B. Pradhan, Landslide Susceptibility Mapping At Golestan Province, Iran: A Comparison Between Frequency Ratio, Dempster-Shafer, and Weights of Evidence Models, J. Asian Earth Sci, Vol. 61, 2012,
pp. 221-236, http://dx.doi.org/10.1016/j.jseaes.2012.10.005.
[13] A. D. Regmi, K. C. Devkota, K. Yoshida,
B. Pradhan, H. R. Pourghasemi, T. Kumamoto,
A. Akgun, Application of Frequency Ratio, Statistical Index, and Weights of Evidence Models and Their Comparison in Landslide Susceptibility Mapping in Central Nepal Himalaya, Arab
J. Geosci, Vol. 7, No. 2, 2014, pp. 725-742, http://dx.doi.org/10.1007/s12517-012-0807-z.
[14] R. K. Dahal, S. Hasegawa, A. Nonomura,
M. Yamanaka, S. Dhakal, P. Paudyal, Predictive Modeling of Rainfall-Induced Landslide Hazard In The Lesser Himalaya of Nepal Based on Weights of Evidence, Geomorphology, Vol. 102, No. 3-4, 2008, pp. 496-510, http://dx.doi.org/10.1016/j.geomorph.2008.05.041.
[15] R. K. Dahal, S. Hasegawa, A. Nonomura,
M. Yamanaka, T. Masuda, K. Nishino, GIS-based Weights of Evidence Modeling of Rainfall-Induced Landslides in Small Catchments For Landslide Susceptibility Mapping, Environ Geol, Vol. 54, No. 2, 2008, pp. 311-324, http://dx.doi.org/10.1007/s00254-007-0818-3.
[16] N. R. Regmi, J. R. Giardino, J. D. Vitek, Modeling Susceptibility to Landslides Using The Weight of Evidence Approach: Westen Colorado, USA, Geomorphology, Vol. 115, No. 1-2, 2010,
pp. 172-187, http://dx.doi.org/10.1016/j.geomorph.2009.10.002.
[17] H. J. Oh, S. Lee, Assessment of Ground Subsidence Using GIS and the Weights of Evidence Model, Eng Geol, Vol. 115, No. 1-2, 2010, pp. 36-48, http://dx.doi.org/10.1016/j.enggeo.2010.06.015.
[18] C. Xu, X. Xu, F. C. Dai, J. Z. Xiao, X. B. Tan,
R. M. Yuan, Landslide Hazard Mapping Using GIS and Weight of Evidence Model in Qingshui River Watershed of 2008 Wenchuan Earthquake Struck Region, J. Earth Sci, Vol. 23, No. 1, 2012,
pp. 97-120, http://dx.doi.org/10.1007/s12583-012-0236-7.
[19] H. R. Pourghasemi, B. Pradhan, C. Gokceoglu,
M. Mohammadi, H. R. Moradi, Application of Weights of Evidence and Certainty Factor Models and Their Comparison In Landslide Susceptibility Mapping At Haraz Watershed, Iran, Arab J. Geosci, Vol. 6, No. 7, 2013, pp. 2351-2365, http://dx.doi.org/10.1007/s12517-012-0532-7.
[20] E. R. Sujatha, P. Kumaravel, G. V. Rajamanickam, Assessing Landslide Susceptibility Using Bayesian Probability-Based Weight of Evidence Model, Bull Eng Geol Environ, Vol. 73, No. 1, 2014,
pp. 147-161,
http://dx.doi.org/10.1007/s10064-013-0537-9.
[21] Q. Q. Wang, W. P. Li, M. L. Xing, Y. L. Wu, Y. B. Pei, D. D. Yang, H. Y. Bai, Landslide Susceptibility Mapping At Gongliu County, China Using Artificial Neural Network and Weight of Evidence Models, Geosci J, Vol. 20, No. 5, 2016, pp. 705-718,
http://dx.doi.org/10.1007/s12303-016-0003-3.
[22] M. D. Hien, K. L. Yin, Z. Z. Guo, A Comparative Study on the Integrative Ability of the Analytical Hierarchy Process, Weights of Evidence And Logistic Regression Methods with the Flow-R Model for Landslide Susceptibility Assessment, Geomatics, Natural Hazard and Risk, Vol. 11,
No. 1, pp. 2449-2485, https://doi.org/10.1080/19475705.2020.1846086.
[23] T. Chen, R. Q. Niu, X. P. Jia, A Comparison of Information Value and Logistic Regression Models In Landslide Susceptibility Mapping By Using GIS, Environ Earth Sci, Vol. 75, No. 10, 2016,
pp. 867,
http://dx.doi.org/10.1007/s12665-016-5317-y.
[24] F. Mengistu, K. V. Suryabhagavan, T. K. Raghuvanshi, E. Lewi, Landslide Hazard Zonation and Slope Instability Assessment Using Optical and Insar Data: A Case Study from Gidole Town and Its Surrounding Areas, Southern Ethiopia, Remote Sensing of Land, Vol. 3, No. 1, 2019,
pp. 1-14, https://doi.org/10.21523/gcj1.19030101.
[25] G. F. Zhang, Y. X. Cai, Z. Zheng, J. W. Zhen,
Y. L. Liu, K. Y. Huang, Integration of the Statistical Index Method and the Analytic Hierarchy Process Technique for the Assessment of Landslide Susceptibility in Huizhou, China, Catena, Vol. 142, 2016, pp. 233-244, http://dx.doi.org/10.1016/j.catena.2016.03.028.
[26] Z. Wu, Y. Wu, Y. Yang, F. Chen, N. Zhang, Y. Ke, W. Li, A Comparative Study on the Landslide Susceptibility Mapping Using Logistic Regression And Statistical Index Models, Arab J. Geosci,
Vol. 10, No. 8, 2017, pp. 1-17, http://dx.doi.org/10.1007/s12517-017-2961-9.
[27] H. J. Oh, S. Lee, G. M. Soedradjat, Quantitative Landslide Susceptibility Mapping at Pemalang Area, Indonesia, Environmental Earth Sciences, Vol. 60, No. 6, 2010, pp. 1317-1328, https://doi.org/10.1007/s12665-009-0272-5.
[28] B. Pradhan, Manifestation of an Advanced Fuzzy Logic Model Coupled With Geo-Information Techniques to Landslide Susceptibility Mapping and Their Comparison with Logistic Regression Modelling, Environ Ecol Stat, Vol. 18, No. 3, 2011, pp. 471-493,
http://dx.doi.org/10.1007/s10651-010-0147-7.
[29] B. Pradhan, 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, Vol. 51, 2013, pp. 350-365, http://dx.doi.org/10.1016/J.Cageo.2012.08.023.
[30] H. R. Pourghasemi, A. G. Jirandeh, B. Pradhan,
C. Xu, C. Gokceoglu, Landslide Susceptibility Mapping Using Support Vector Machine and GIS at the Golestan Province, J Earth Syst Sci,
Vol. 122, No. 2, 2013, pp. 349-369, https://doi.org/10.1007/S12040-013-0282-2.
[31] H. Y. Hong, B. Pradhan, C. Xu, T. B. Dieu, Spatial Prediction of Landslide Hazard at the Yihuang Area (China) Using Two-Class Kernel Logistic Regression, Alternating Decision Tree and Support Vector Machines, Catena, Vol. 133, 2015,
pp. 266-281, http://dx.doi.org/10.1016/j.catena.2015.05.019.
[32] B. T. Dieu, H. Shahabi, A. Shirzadi, K. Chapi,
B. Pradhan, W. Chen, K. Khosravi, M. Panahi,
B. B. Ahmad, L. Saro, Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms, Sensors, Vol. 18, No. 8, 2018, pp. 1-20, https://doi.org/10.3390/S1808246.
[33] L. Ermini, F. Catani, N. Casagli, Artificial Neural Networks Applied to Landslide Susceptibility Assessment, Geomorphology, Vol. 66, 2005,
pp. 327-343, https://doi.org/10.1016/j.geomorph.2004.09.025.
[34] H. A. Nefeslioglu, C. Gokceoglu, H. Sonmez, An Assessment on the Use of Logistic Regression and Artificial Neural Networks with Different Sampling Strategies for the Preparation of Landslide Susceptibility Maps, Eng Geol, Vol. 97, No. 3-4, 2008, pp. 171-91, https://doi.org/10.1016/j.enggeo.2008.01.004.
[35] B. Pradhan, S. Lee, Landslide Risk Analysis Using Artificial Neural Network Model Focussing on Different Training Sites, Int J Physsci, Vol. 4, 2009, pp. 001-015, https://doi.org/10.5897/ijps.9000343.
[36] B. Pradhan, S. Lee, Delineation Of Landslide Hazard Areas on Penang Island, Malaysia, By Using Frequency Ratio, Logistic Regression, and Artificial Neural Network Models, Environmental Earth Sciences, Vol. 60, No. 5, 2010, pp. 1037-1054, https://doi.org/10.1007/s12665-009-0245-8.
[37] B. Pradhan, S. Lee, Landslide Susceptibility Assessment and Factor Effect Analysis: Backpropagation Artificial Neural Networks and Their Comparison with Frequency Ratio and Bivariate Logistic Regression Modelling, Environ ModellSoftw, Vol. 25, 2010, pp. 747-59, https://doi.org/10.1016/j.envsoft.2009.10.016.
[38] I. Yilmaz, Comparison of Landslide Susceptibility Mapping Methodologies For Koyulhisar, Turkey: Conditional Probability, Logistic Regression, Artificial Neural Networks, and support vector machine, Environmental Earth Sciences, Vol. 61, No. 4, 2010, pp. 821-836, https://doi.org/10.1007/s12665-009-0394-9.
[39] P. T. Binh, B. Pradhan, T. D. Bui, I. Prakash, M. B. Dholakia, A Comparative Study of Different Machine Learning Methods for Landslide Susceptibility Assessment: A Case Study of Uttarakhand Area (India), Environmental Modelling & Software, Vol. 84, 2016, pp. 240-250, https://doi.org/10.1016/j.envsoft.2016.07.005.
[40] J. Corominas, The Angle of Reach as Amobility Index For Small And Large Landslides, Canadian Geotechnical Journal, Vol. 33, No. 2, 1996,
pp. 260–271, https://doi.org/10.1139/t96-005.
[41] F. Legros, Themobility of Long-Runout Landslides, Engineering Geology, Vol. 63, 2002, pp. 301-331,
https://doi.org/10.1016/S0013-7952(01)00090-4.
[42] R. M. Iverson, S. P. Schilling, J. W. Vallance, Objective Delineation of Lahar-Inundation Hazard Zones, Geological Society of America Bulletin, Vol. 110, No. 8, 1998, pp. 972-984, https://doi.org/10.1130/0016-7606(1998)110<0972:ODOLIH>2.3.CO;2.
[43] G. B. Crosta, S. Cucchiaro, P. Frattini, Validation of Semi-Empirical Relationships for the Definition of Debris-flow Behavior In Granular Materials, in: Rickenmann, D., Chen, C. (Eds.), 3rd Int. Conf. on Debris-Flow Hazards Mitigation, Millpress, Davos, 2003, pp. 821-831.
[44] R. M. Iverson, The Physics of Debris Fows, Review of Geophysics, Vol. 35, No. 3, 1997,
pp. 245-296, https://doi.org/10.1029/97RG00426.
[45] M. Hürlimann, D. Rickenmann, V. Medina, A Bateman, Evaluation of Approaches to Calculate Debris-Flow Parameters for Hazard Assessment, Engineering Geology, Vol. 102, 2008, pp. 152-163, https://doi.org/10.1016/j.enggeo.2008.03.012.
[46] D. Rickenmann,D. Laigle, B. McArdell, J. Hübl, Comparison of 2D Debris-flow Simulation Models with field Events, Computational Geosciences, Vol. 10, No. 2, 2006, pp. 24-264, https://doi.org/10.1007/s10596-005-9021-3.
[47] K. Schraml, B. Thomschitz, B. W. McArdell,
C. Graf, R. Kaitna, Modeling Debris-flow Runout Patterns on Two Alpine Fans With Different Dynamic Simulation Models, Nat Hazards Earth Syst Sci, Vol. 15, 2015, pp. 1483-1492, https://doi.org/10.5194/nhess-15-1483-2015.
[48] J. Blahut, P. Horton, S. Sterlacchini,
M. Jaboyedoff, Debris Fow Hazard Modelling on Medium Scale: Valtellina di Tirano, Italy, Nat. Hazards Earth Syst Sci, Vol. 10, 2010,
pp. 2379-2390, https://doi.org/10.5194/nhess-10-2379-2010.
[49] P. Horton, M. Jaboyedoff, M. Zimmermann,
B. Mazotti, C. Longchamp, Flow-R, a Model for Debris Flow Susceptibility Mapping at A Regional Scale – Some Case Studies, 5th Int Conf on Debris-Flow Hazards Mitigation, Padua, Italy, Italian Journal of Engineering Geology and Environment, 2011, pp. 875-884.
[50] P. Horton, M. Jaboyedoff, B. Rudaz, M. Zimmermann, Flow-R, a Model for Susceptibility Mapping of Debris Flows and Other Gravitational Hazards At A Regional Scale, Nat Hazards Earth Syst Sci, Vol. 13, No. 4, 2013, pp. 869-885, http://dx.doi.org/10.5194/nhess-13-869-2013.
[51] M. Jaboyedoff, Ch. Choffet, M. H. Derron,
P. Horton, A. Loye, C. Longchamp, B. Mazotti,
C. Michoud, A. Pedrazzini, Preliminary Slope Mass Movements Susceptibility Mapping Using DEM and LiDAR DEM, in: Terrigenous Mass Movements: Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology, Edited by: Pradhan, B. And Buchroithner, M., Springer-Verlag, Berlin Heidelberg, Germany, 2012, pp. 109-170, https://doi.org/10.1007/978-3-642-25495-6_5.
[52] B. Q. Luna, J. Blahut, M. Kappes, S. O. Akbas,
J. P. Malet, A. Remaitre, T. V. Asch, M. Jaboyedoff, Chapter 5: Methods for Debris Flow Hazard and Risk Assessment, in Van Asch T Ed, Mountain Risks: From Prediction To Management and Governance, Advances in Natural and Technological Hazards Research, Vol. 34, 2014, http://dx.doi.org/10.1007/978-94-007-6769-05.
[53] B. Q. Luna, J. Blahut, C. Camera, C. Van Westen, T. Apuani, V. Jetten, S. Sterlacchini, Physically Based Dynamic Run-Out Modelling for Quantitative Debris Flow Risk Assessment: A Case Study in Tresenda, Northern Italy, Environ Earth Sci, Vol. 72, 2014, pp. 645-661, http://dx.doi.org/10. 1007/s12665-013-2986-7.
[54] B. Q. Luna, J. Blahut, T. V. Asch, C. V. Westen, M. Kappes, ASCHFLOW: A Dynamic Landslide Run-Out Model for Medium Scale Hazard Analysis, Geoenviron Disasters, Vol. 3, 2016,
pp. 1-17,
http:// dx.doi.org/10.1186/s40677-016-0064-7.
[55] S. H. Kang, S. R. Lee, N. Nikhil, J. Y. V. Park,
D. H. Lee, Development of An Initiation Criterion for Debris Flows Based on Local Topographic Properties and Applicability Assessment at A Regional Scale, Eng Geol, Vol. 230, 2017, pp. 64-76, http://dx.doi.org/10.1016/j.enggeo.2017.09.017.
[56] S. Kang, S. R. Lee, Debris Flow Susceptibility Assessment Based on An Empirical Approach in The Central Region of South Korea, Geomorphology, Vol. 308, 2018, pp. 1-12, https://doi.org/10.1016/j.geomorph.2018.01.025.
[57] N. T. Long, F. D. Smedt, Application of an Analytical Hierarchical Process Approach for Landslide Susceptibility Mapping in A Luoi District, Thua Thien Hue Province Vietnam, Environ Earth Sci, Vol. 66, 2012, pp. 1739-1752, http://dx.doi.org/10.1007/s12665-011-1397-x.
[58] L. Q. Hung, N. T. H. Van, M. D. Duc, L. T. C. Ha, P. V. Son, N. H. Khanh, L. T. Binh, Landslide Susceptibility Mapping by Combining the Analytical Hierarchy Process and Weighted Linear Combination Methods: A Case Study in the Upper Lo River Catchment (Vietnam), Landslides,
Vol. 13, 2016, pp. 1285-1301,
https://doi.org/ 10.1007/s10346-015-0657-3.
[59] N. T. Long, F. D. Smedt, Analysis and Mapping of Rainfall-Induced Landslide Susceptibility in A Luoi District, Thua Thien Hue Province, Vietnam, Water-MDPI, Vol. 11, 2019, https://doi.org/10.3390/w11010051.
[60] B. T. Dieu, B. Pradhan, O. Lofman, I. Revhaug,
O. B. Dick, Spatial Prediction of Landslide Hazards in Hoabinh Province (Vietnam): A Comparative Assessment of the Efficacy of Evidential Belief Functions And Fuzzy Logic Models, Catena, Vol. 96, 2012, pp. 28-40, https://doi.org/10.1016/j.catena.2012.04.001.
[61] B. T. Dieu, B. Pradhan, O. Lofman, I. Revhaug,
O. B. Dick, Landslide Susceptibility Assessment in The Hoabinh Province of Vietnam: A Comparison of the Levenberg-Marquardt and Bayesian Regularized Neural Networks, Geomorphology, Vol. 171-172, 2012, pp. 12-29, https://doi.org/10.1016/j.geomorph.2012.04.023.
[62] B. T. Dieu, P. T. Binh, Q. P. Nguyen, N. D. Hoang, Spatial Prediction of Rainfall-Induced Shallow Landslides Using Hybrid Integration Approach of Least-Squares, Support Vector Machines And Differential Evolution Optimization: A Case Study In Central Vietnam, Int J Digital Earth, 2016,
pp. 1-21, https://doi.org/10.1080/17538947.2016.1169561.
[63] P. T. Binh, B. Pradhan, B. T. Dieu, Spatial Prediction of Landslides Using A Hybrid Machine Learning Approach Based on Random Subspace and Classification and Regression Trees, Geomorphology, Vol. 303, 2018, pp. 256-270, https://doi.org/10.1016/j.geomorph.2017.12.008.
[64] P. T. Binh, N. T. Thoi, C. C. Qi, T. V. Phong,
J. Dou, H. S. Lanh, L. V. Hiep, I. Prakash., Coupling RBF Neural Network with Ensemble Learning Techniques for Landslide Susceptibility Mapping, Catena, Vol. 195, https://doi.org/10.1016/j.catena.2020.104805.
[65] Vietnam Meteorological and Hydrological Administration, http://vmha.gov.vn/public/kttv-voi-san-xuat-va-doi-song-106/nhieu-hiem-nguy-thien-tai-rinh-rap-nguoi-dan-vung-cao-lao-cai-12248.html (accessed on: May 9th, 2022).
[66] Laocai Radio and Television, http://laocaitv.vn/lu-quet/sat-lo-dat-khien-2-nguoi-tu-vong-o-xa-phin-ngan-huyen-bat-xat (accessed on: May 9th 2022).
[67] N. X. Khien et al., Assess the Current Status of Geological Hazards in Ha Giang, Cao Bang, Tuyen Quang and Bac Kan Provinces, Identify Causes, Forecast and Propose Measures to Prevent and Minimize Consequences, Vietnam Institute of Geosciences and Mineral Resources, 2011
(in Vietnamese).
[68] O. F. Althuwaynee, B. Pradha, H. J. Park, J. H. Lee, A Novel Ensemble Bivariate Statistical Evidential Belief Function with Knowledge-Based Analytical Hierarchy Process and Multivariate Statistical Logistic Regression for Landslide Susceptibility Mapping, Catena, Vol. 114, 2014, pp. 21-36,
http:// dx.doi.org/10.1016/j.catena.2013.10.011.
[69] E. Yesilnacar, T. Topal, Landslide Susceptibility Mapping: A Comparison of Logistic Regression and Neural Networks Methods In A Medium Scale Study, Hendek Region (Turkey), Eng Geol,
Vol. 79, No. 3-4, 2005, pp. 251-266, http://dx.doi.org/10.1016/j.enggeo.2005.02.002.
[70] H. Y. Hong, H. R. Pourghasemi, Z. S. Pourtaghi, Landslide Susceptibility Assessment in Lianhua County (China): A Comparison Between A Random Forest Data Mining Technique and Bivariate and Multivariate Statistical Models, Geomorphology, Vol. 259, 2016, pp. 105-118, http://dx.doi.org/10.1016/j.geomorph.2016.02.012.
[71] A. M. S. Pradhan, Y. T. Kim, Relative Effect Method of Landslide Susceptibility Zonation in Weathered Granite Soil: A Case Study in Deokjeok-Ri Creek, Nat Hazards, Vol. 72, 2014, pp. 1189-1217, http://dx. doi.org/10.1007/s11069-014-1065-z.
[72] A. M. S. Pradhan, Y. T. Kim, Spatial Data Analysis And Application of Evidential Belief Functions to Shallow Landslide Susceptibility Mapping At Mt, Umyeon, Seoul, Korea, Bull Eng Geol Environ, Vol. 76, No. 4, 2017, pp. 1263-1279, http://dx.doi.org/10.1007/s10064-016-0919-x.
[73] K. Beven, M. Kirkby, Un Modèle À Base Physique De Zone D'appel Variable De L'hydrologie Du Bassin Versant (A Physically Based, Variable Contributing Area Model of Basin Hydrology), Hydrol Sci J, Vol. 24, 1979, pp. 43-69.
[74] I. Yilmaz, Landslide Susceptibility Mapping Using Frequency Ratio, Logistic Regression, Artificial Neural Networks and Their Comparison: A Case Study from Kat Landslides (Tokat–Turkey), Comput Geosci, Vol. 35, 2009, pp. 1125-1138, https://doi.org/10.1016/j.cageo.2008.08.007.
[75] A. M. S. Pradhan, H. S. Kang, S. Lee, Y. T. Kim, Spatial Model Integration For Shallow Landslide Susceptibility and Its Runout Using A GIS-Based Approach in Yongin, Geocarto Int, 2016, https://doi.org/10. 1080/10106049.2016.1155658.
[76] I. D. Moore, G. J. Burch, Physical Basis of the Length–Slope Factor in the Universal Soil Loss Equation, Soil Sci Soc Am J, Vol. 50, No. 5, 1986, pp. 1294-1298, https://doi.org/10.2136/sssaj1986. 03615995005000050042x.
[77] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
[78] N. Friedman, D. Geiger, M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, Vol. 29, 1997, pp. 131-163, https://doi.org/10.1023/a:1007465528199.
[79] B. G. Marrcot, J. D. Steventon, G. D. Sutheland,
R. K. Mccann, Guidelines for Developing and Updating Bayesian Belief Networks Applied to Eological Modeling and Conservation, Can J for Res, Vol. 36, No. 12, 2006, pp. 3063-3074, https://doi.org/10.1139/x06-135.
[80] Y. Song, J. Gong, S. Gao, D. Wang, T. Cui, Y. Li, B. Wei, Susceptibility Assessment of Earthquake-Induced Landslides Using Bayesian Network: A Case Study in Beichuan China, Comput. Geosci, Vol. 42, 2012, pp. 189-199, https://doi.org/10.1016/j.cageo.2011.09.011.
[81] B. Pradhan, Manifestation of An Advanced Fuzzy Logic Model Coupled with Geo-Information Techniques to Landslide Susceptibility Mapping and Their Comparison with Logistic Regression Modelling, Environ Ecol Stat, Vol. 18, No. 3, 2011, pp. 471-493,
http://dx.doi.org/10.1007/s10651-010-0147-7.
[82] F. C. Dai, C. .F Lee, J. Li, Z. W.Xu, Assessment of Landslide Susceptibility on the Natural Terrain of Lantau Island, Hong Kong, Environ Geol, Vol. 40, 2001, pp. 381-391, https://doi.org/10.1007/S002540000163.
[83] C. T. Lee, C. C. Huang, J. F. Lee, K. L. Pan, M. N. Lin, J. J. Dong, Statistical Approach to Storm Event-Induced Landslides Susceptibility, Nat Hazard Earth Syst Sci, Vol. 8, No. 4, 2008, pp. 941-960, https://doi.org/10.5194/nhess-8-941-2008.
[84] W. R. Klecka, Discriminant Analysis, Sage University Paper Series on Quantitative Applications in the Social Sciences, Sage Publications, Beverly Hills and London, 1980,
pp. 07-19.
[85] J. C. Davis, Statistics and Data Analysis in Geology, (3rd Ed), Johnwiley And Sons, New York, 2002.
[86] C. Liu, W. Y. Li, H. B. Wu, P. Lu, K. Sang, W. W. Sun, W. Chen, Y. Hong, R. X. Li, Susceptibility Evaluation and Mapping of China’s Landslides Based on Multi-Source Data, Nat. Hazards,
Vol. 69, 2013, pp. 1477-1495, https://doi.org/10.1007/S11069-013-0759-Y.
[87] S. Lee, J. H. Ryu, M. J. Lee, J. S. Won, The Application of Artificial Neural Networks to Landslide Susceptibility Mapping At Janghung, Korea, Mathematical Geology, Vol. 38, 2006, https://doi.org/10.1007/s11004-005-9012-x.
[88] A. H. Fath, F. Madanifar, M. Abbasi, Implementation of Multilayer Perceptron (MLP) And Radial Basis Function (RBF) Neural Networks to Predict Solution Gas-Oil Ratio of Crude Oil Systems, Petroleum, Vol. 6, 2020,
pp. 80-91, https://doi.org/10.1016/j.petlm.2018.12.002.
[89] V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd, Springer, New York, 1999.
[90] B. Schölkopf, A. Smola, Learning with Kernels: Support Vectormachines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, 2002.
[91] J. M. Moguerza, A. Munoz, Support Vector Machines With Applications, Stat Sci, Vol. 21, 2006, pp. 322-336,
https://doi.org 1214/088342306000000493.
[92] V. Cherkassky, F. Mulier, Learning From Data: Concepts, Theory, and Methods, Wiley, New York, 2007.
[93] T. Kavzoglu, E. K. Sahin, I. Colkesen, Landslide Susceptibility Mapping Using GIS-Basedmulti-Criteria Decision Analysis, Support Vector Machines, and Logistic Regression, Landslides, Vol. 11, No. 3, 2014, pp. 425-439, https://doi.org/10.1007/s10346-013-0391-7.
[94] J. A. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers, Neural Process Lett, Vol. 9, No. 3, 1999, pp. 293-300, https://doi.org/10.1023/a:1018628609742.
[95] B. Dixon, N. Candade, Multispectral Landuse Classification Using Neural Networks and Support Vector Machines: One or the Other, or Both?, Int J Remote Sens, Vol. 29, No. 4, 2008, pp. 1185-1206, https://doi.org/10.1080/01431160701294661.
[96] P. Kayastha, M. R. Dhital, F. D. Smedt
F, Application of the Analytical Hierarchy Process (AHP) for Landslide Susceptibility Mapping: A Case Study from the Tinau Watershed, West Nepal, Comput Geosci, Vol. 52, 2013,
pp. 398-408, http://dx.doi.org/10.1016/j.cageo.2012.11.003.
[97] C. J. F. Chung, A. G. Fabbri, Validation of Spatial Prediction Models for Landslide Hazard Mapping, Natural Hazards, Vol. 30, No. 3, 2003, pp. 451-472, https://doi.org/10.1023/b:nhaz.0000007172.62651.2b.
[98] H. R. Pourghasemi, H. R. Moradi, S. M. Fatemi Aghda, C. Gokceoglu, B. Pradhan, GIS-based Landslide Susceptibility Mapping with Probabilistic Likelihood Ratio and Spatial Multi-Criteria Evaluation Models (North of Tehran, Iran), Arab J Geosci, Vol. 7, No. 5, 2014, pp. 1857-1878, http://dx.doi.org/10.1007/s12517-012-0825-x.
[99] R. Talaei, Landslide Susceptibility Zonation Mapping Using Logistic Regression and Its Validation in Hashtchin Region, Northwest of Iran, Journal Geological Society of India, Vol. 84, 2014, pp. 68-86,
https://doi.org/10.1007/s12594-014-0111-5.
[100] J. W. Choi, H. J. Oh, H. J. Lee, C. W. Lee, S. Lee, Combining Landslide Susceptibility Maps Obtained from Frequency Ratio, Logistic Regression and Artificial Neural Network Models Using ASTER Images and GIS, Engineering Geology, Vol. 124, 2012, pp. 12-23, https://doi.org/10.1016/j.enggeo.2011.09.011.
[101] J. J. Dong, Y. Y. Tung, C. C. Chen, J. J. Liao,
Y. W. Pan, Discriminant Analysis of Theo Geomorphic Characteristics and Stability of Landslide Dams, Geomorphology, Vol. 110, 2009, pp. 162-171, https://doi.org/10.1016/j.geomorph.2009.04.004.
[102] S. W. He, P. Pan, L. Dai, H. J. Wang, J. P. Liu, Application of Kernel-Based Fisher Discriminant Analysis to Map Landslide Susceptibility In The Qinggan River Delta, Three Gorges, China, Geomorphology, Vol. 171-172, 2012, pp. 30-41, https://doi.org/10.1016/j.geomorph.2012.04.024.