Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco
The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901).
 Guzzetti, F., 2005. Landslide hazard and risk assessment (Ph.D. Thesis), University of Bonn, Bonn (371 pp.).
 Varnes, D. J., 1978. Slope movement types and processes. Transportation Research Board Special Report 176. Transportation Research Board, Washington DC, USA (11–33 pp.).
 Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., 2008. 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. 97, 171–191.
 Shahabi, Himan, Saeed Khezri, Baharin Bin Ahmad, and Mazlan Hashim. 2014. “Landslide Susceptibility Mapping at Central Zab Basin, Iran: A Comparison between Analytical Hierarchy Process, Frequency Ratio and Logistic Regression Models.” catena 115 (April): 55–70. https://doi.org/10.1016/j.catena.2013.11.014.
 Aghda. SM Fatemi; V. Bagheri. M. Razifard (2018) Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region, Tehran, Iran S. M. Geotech Geol Eng (2018) 36: 915–937. https://doi.org/10.1007/s10706-017-0365-y.
 Torizin. Jewgenij; Michael Fuchs; Adnan Alam Awan; Ijaz Ahmad; Sardar Saeed Akhtar; Simon Sadiq; Asif Razzak; Daniel Weggenmann; Faseeh Fawad; Nimra Khalid; Faisan Sabir; Ahsan Jamal Khan. 2017. Statistical landslide susceptibility assessment of the Mansehra and Torghar districts, Khyber Pakhtunkhwa Province, Pakistan. Nat Hazards (2017) 89: 757–784 DOI 10.1007/s11069-017-2992-2.
 Tsangaratos Paraskevas; Ioanna Ilia; Haoyuan Hong; Wei Chen; Chong Xu. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides (2017) 14: 1091–1111 DOI 10.1007/s10346-016-0769-4.
 Westen, C. J. van, N. Rengers, and R. Soeters. 2003. “Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment.” Natural Hazards 30 (3): 399–419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e.
 Song, Jae-Joon, Chung-In Lee, and Masahiro Seto. 2001. “Stability Analysis of Rock Blocks around a Tunnel Using a Statistical Joint Modeling Technique.” Tunnelling and Underground Space Technology 16 (4): 341–351. https://doi.org/10.1016/S0886-7798(01)00063-3.
 Bai, Shi-Biao, Jian Wang, Guo-Nian Lü, Ping-Gen Zhou, Sheng-Shan Hou, and Su-Ning Xu. 2010. “GIS-Based Logistic Regression for Landslide Susceptibility Mapping of the Zhongxian Segment in the Three Gorges Area, China.” Geomorphology 115 (1): 23–31. https://doi.org/10.1016/j.geomorph.2009.09.025.
 Kanungo, D. P., M. K. Arora, S. Sarkar, and R. P. Gupta. 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): 347–366. https://doi.org/10.1016/j.enggeo.2006.03.004.
 Lee, Saro, Joo-Hyung Ryu, and Ii-Soo Kim. 2007. “Landslide Susceptibility Analysis and Its Verification Using Likelihood Ratio, Logistic Regression, and Artificial Neural Network Models: Case Study of Youngin, Korea.” Landslides 4 (4): 327–338. https://doi.org/10.1007/s10346-007-0088-x.
 Pourghasemi, Hamid Reza, Biswajeet Pradhan, and Candan Gokceoglu. 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.
 Nourani, Vahid, Biswajeet Pradhan, Hamid Ghaffari, and Seyed Saber Sharifi. 2014. “Landslide Susceptibility Mapping at Zonouz Plain, Iran Using Genetic Programming and Comparison with Frequency Ratio, Logistic Regression, and Artificial Neural Network Models.” Natural Hazards 71 (1): 523–547. https://doi.org/10.1007/s11069-013-0932-3.
 Yilmaz, Işık. 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.
 Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. 1995. “Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data.” Remote Sensing of Environment 54 (2): 127–140. https://doi.org/10.1016/0034-4257(95)00137-P.
 Akgun, A., E. A. Sezer, H. A. Nefeslioglu, C. Gokceoglu, and B. Pradhan. 2012. “An Easy-to-Use MATLAB Program (MamLand) for the Assessment of Landslide Susceptibility Using a Mamdani Fuzzy Algorithm.” Computers & Geosciences 38 (1): 23–34. https://doi.org/10.1016/j.cageo.2011.04.012.
 Das, Iswar, Sashikant Sahoo, Cees van Westen, Alfred Stein, and Robert Hack. 2010. “Landslide Susceptibility Assessment Using Logistic Regression and Its Comparison with a Rock Mass Classification System, along a Road Section in the Northern Himalayas (India).” Geomorphology 114 (4): 627–637. https://doi.org/10.1016/j.geomorph.2009.09.023.
 Nandi, A., and A. Shakoor. 2010. “A GIS-Based Landslide Susceptibility Evaluation Using Bivariate and Multivariate Statistical Analyses.” Engineering Geology 110 (1): 11–20. https://doi.org/10.1016/j.enggeo.2009.10.001.
 Atkinson, P. M., and R. Massari. 2011. “Autologistic Modelling of Susceptibility to Landsliding in the Central Apennines, Italy.” Geomorphology, Scale Issues in Geomorphology, 130 (1): 55–64. https://doi.org/10.1016/j.geomorph.2011.02.001.
 Demir, Gokhan, Mustafa Aytekin, and Aykut Akgun. 2015. “Landslide Susceptibility Mapping by Frequency Ratio and Logistic Regression Methods: An Example from Niksar–Resadiye (Tokat, Turkey).” Arabian Journal of Geosciences 8 (3): 1801–1812. https://doi.org/10.1007/s12517-014-1332-z.
 Friedman, Jerome H. 1991. “Multivariate Adaptive Regression Splines.” The Annals of Statistics 19 (1): 1–67.
 Felicísimo, Ángel M., Aurora Cuartero, Juan Remondo, and Elia Quirós. 2013. “Mapping Landslide Susceptibility with Logistic Regression, Multiple Adaptive Regression Splines, Classification and Regression Trees, and Maximum Entropy Methods: A Comparative Study.” Landslides 10 (2): 175–189. https://doi.org/10.1007/s10346-012-0320-1.
 Booth, G. D., M. J. Niccolucci, and E. G. Schuster. 1994. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. USDA. For. Serv. Res. Pap. INT-470.
 Schuerman J (1983) Principal components analysis. Multivariate analysis in the human services. Springer, Netherlands, pp 93–119.
 Belsley, David A. 1991. “A Guide to Using the Collinearity Diagnostics.” Computer Science in Economics and Management 4 (1): 33–50. https://doi.org/10.1007/BF00426854.
 Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis. Prentice Hall, New York.
 Liao D, Valliant R (2012) Variance inflation factors in the analysis of complex survey data. Surv Methodol 38:53–62
 Yesilnacar, E., and T. Topal. 2005. “Landslide Susceptibility Mapping: A Comparison of Logistic Regression and Neural Networks Methods in a Medium Scale Study, Hendek Region (Turkey).” Engineering Geology 79 (3): 251–266. https://doi.org/10.1016/j.enggeo.2005.02.002.
 Fawcett, Tom. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters, ROC Analysis in Pattern Recognition, 27 (8): 861–874. https://doi.org/10.1016/j.patrec.2005.10.010.