Volume 9, Issue 18 (1-2019)                   jwmr 2019, 9(18): 208-219 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Timoori Yansari Z, Hosseinzadeh S R, Kavian A, Pourghasemi H R. (2019). Comparison of Landslide Susceptibility Maps using Logistic Regression (LR) and Generalized Additive Model (GAM) . jwmr. 9(18), 208-219. doi:10.29252/jwmr.9.18.208
URL: http://jwmr.sanru.ac.ir/article-1-723-en.html
Faculty of Letters and Humanities, Ferdowsi University of Mashhad
Abstract:   (3353 Views)

Landslide is one of the most common natural disasters that endanger the lives and properties of people in mountainous areas. Therefore, identification of risk exposure areas of landslide is essential to prevent and reduce damages by landslides. The purpose of this study is compared to logistic regression (LR) and generalized additive models (GAM) and the evaluation of their performance for landslide susceptibility mapping in the Chahardangeh Watershed, Mazandaran Province. At the first, landslide locations were identified by Google Earth images and extensive field survey. Then, the landslide inventory map was randomly divided as training data 70% for modeling and the remaining 30% was applied for the model validation. The landslide conditioning factors including topographic, hydrologic, geology and human factors were constructed in GIS. Finally, the receiver operating characteristic (ROC) Curve was used for the model validation. The validation of results showed that the area under the ROC curve for LR and GAM models were 81.2% and 82.4%, respectively. So, both of the models are suitable and efficient methods for landslide susceptibility mapping in the study area. Although, the obtained results showed that the GAM model performed is slightly better than the LR model for determining regions of susceptible to occurrence of landslide in the study area.
                                                                                

Full-Text [PDF 1909 kb]   (900 Downloads)    
Type of Study: Research | Subject: بلايای طبيعی (سيل، خشکسالی و حرکت های توده ای)
Received: 2016/11/20 | Revised: 2019/01/21 | Accepted: 2017/02/13 | Published: 2019/01/21

References
1. Ahmadi, H. 2005. Applied Geomorphology. Vol. 1, Water erosion, University of Tehran Press, 688 pp. (In Persian).
2. Atkinson, P.M. and R. Massari.1998. Generalised linear modeling of susceptibility to landsliding in the Central Apennines, Italy. Computers & Geosciences, 24(4): 373‐385. [DOI:10.1016/S0098-3004(97)00117-9]
3. Brenning, A., M. Schwinn, A.P. Ruiz-Páez and J. Muenchow. 2014. Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province. Natural Hazards and Earth System Sciences Discussions, 2(3): 1945-1975. [DOI:10.5194/nhessd-2-1945-2014]
4. Budimir, A., P.M. Atkinson and H.G. Lewis. 2015. A systematic review of landslide probability mapping using logistic regression. Landslides, 12(3): 419-436.‌ [DOI:10.1007/s10346-014-0550-5]
5. Choi, J., H.J. Oh, H.J. Lee, C. Lee and S. Lee. 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124: 12-23.‌ [DOI:10.1016/j.enggeo.2011.09.011]
6. Colkesen, I., E.K. Sahin and T. Kavzoglu. 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.‌ [DOI:10.1016/j.jafrearsci.2016.02.019]
7. Dahal, R.K., S. Hasegawa, A. Nonomura, M. Yamanaka, T. Masuda and K. Nishino. 2008. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54(2): 311-324. [DOI:10.1007/s00254-007-0818-3]
8. Das, I., A. Stein, N. Kerle and V.K. Dadhwal. 2012. Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology, 179: 116-125.‌ [DOI:10.1016/j.geomorph.2012.08.004]
9. Devkota, K.C., A.D. Regmi, H.R. Pourghasemi, K. Yoshida, B. Pradhan, I.C. Ryu and O.F. Althuwaynee. 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.‌ [DOI:10.1007/s11069-012-0347-6]
10. Ercanoglu, M. and F.A. Temiz. 2011. Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environmental Earth Sciences, 64(4): 949-964.‌ [DOI:10.1007/s12665-011-0912-4]
11. Fang, X. 2008. Generalized additive models with correlated data. ProQuest, 137pp, ISBN: 0549950907, 9780549950905.
12. Goetz, J.N., R.H. Guthrie and A. Brenning. 2011. Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129(3): 376-386.‌ [DOI:10.1016/j.geomorph.2011.03.001]
13. Goetz, J.N., A. Brenning, H. Petschko and P. Leopold. 2016. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences, 81: 1-11.‌ [DOI:10.1016/j.cageo.2015.04.007]
14. Guo, C., D.R. Montgomery, Y. Zhang, K. Wang and Z. Yang. 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology, 248: 93-110. [DOI:10.1016/j.geomorph.2015.07.012]
15. Guzzetti, F., A. Carrara, M. Cardinali and P. Reichenbach. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1): 181-216. [DOI:10.1016/S0169-555X(99)00078-1]
16. Hastie, T.J. and R. Tibshirani. 1990. Generalized additive models. Chapman & Hall, London, 352 pp.
17. Hengl, T. and H. Reuter. 2009. Geomorphometry: concepts, software, applications. 1st ed. Elsevier, Amsterdam, 772 pp.
18. Hjort, J. and M. Luoto. 2012. Can geodiversity be predicted from space? Geomorphology, 153(154): 74-80.‌ [DOI:10.1016/j.geomorph.2012.02.010]
19. Hjort, J. and M. Luoto. 2013. Statistical methods for geomorphic distribution modeling. Treatise on Geomorphology, Academic Press, San Diego, 59-73.‌ [DOI:10.1016/B978-0-12-374739-6.00028-2]
20. Holec, J., M. Bednarik, M. Šabo, J. Minár, I. Yilmaz and M. Marschalko. 2013. A small-scale landslide susceptibility assessment for the territory of Western Carpathians. Natural Hazards, 69(1): 1081-1107.‌ [DOI:10.1007/s11069-013-0751-6]
21. Hong, H., H.R. Pourghasemi and Z.S. Pourtaghi. 2016. Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259: 105-118.‌ [DOI:10.1016/j.geomorph.2016.02.012]
22. Jaafari, A., A. Najafi, H.R. Pourghasemi, J. Rezaeian and A, Sattarian. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4): 909-926.‌ [DOI:10.1007/s13762-013-0464-0]
23. Kemcal, C., A. Akgun and M.Y. Koca. 2009. Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environmental Earth Sciences, 59:745-756. [DOI:10.1007/s12665-009-0070-0]
24. Lehmann, A., J.M. Overton and J.R. Leathwick. 2002. GRASP: generalized regression analysis and spatial prediction. Ecological modelling, 157(2-3): 189-207.‌ [DOI:10.1016/S0304-3800(02)00195-3]
25. O'Brien, R.M. 2007. A caution regarding rules of thumb for variance inflation factors Quality & Quantity, 41(5): 673-690. [DOI:10.1007/s11135-006-9018-6]
26. Park, N.W. and K.H. Chi. 2008. Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive model International Journal of Remote Sensing, 29(1): 247-264.‌ [DOI:10.1080/01431160701227661]
27. Park, N.W. 2011. Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis Environmental Earth Sciences, 62(2): 367-376.‌ [DOI:10.1007/s12665-010-0531-5]
28. Park, S., C. Choi, B. Kim and J. Kim. 2013. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental earth sciences, 68(5): 1443-1464.‌ [DOI:10.1007/s12665-012-1842-5]
29. Petschko, H., A. Brenning, R. Bell, J. Goetz and T. Glade. 2014. Assessing the quality of landslide susceptibility maps-case study Lower Austria. Natural Hazards and Earth System Sciences, 14(1): 95-118.‌ [DOI:10.5194/nhess-14-95-2014]
30. Pourghasemi, H. R., H. R. Moradi and S. F. Aghda. 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.‌ [DOI:10.1007/s11069-013-0728-5]
31. Pourghasemi, H. R and N. Kerle. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environmental earth sciences, 75(3): 185.‌ [DOI:10.1007/s12665-015-4950-1]
32. Pourghasemi, H.R. and M. Rossi. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, North of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theoretical and Applied Climatology, 130(1-2): 609-633.‌ [DOI:10.1007/s00704-016-1919-2]
33. Rasai, A., K. Khosravi, M. Habibnejad Roshan, A. Heidari and A. Mashayekhan. 2015. Lnadslide Hazard Zonation using Multivariate Regression in GIS Environment (Case Study: Aghmashhad Watershed, Mazandaran). Watershed Management Research, 12: 205-215. (In Persian).
34. Regmi, A.D., K.C. Devkota, K. Yoshida, B. Pradhan, H.R. Pourghasemi, T. Kumamoto and A. Akgun. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7(2): 725-742.‌ [DOI:10.1007/s12517-012-0807-z]
35. Ren, F. and X. Wu. 2014. GIS-Based Landslide Susceptibility Mapping Using Remote Sensing Data and Machine Learning Methods. M. Buchroithner et al. (eds.), Cartography from Pole to Pole, Lecture Notes in Geoinformation and Cartography, Springer-Verlag Berlin Heidelberg, 319-333. [DOI:10.1007/978-3-642-32618-9_23]
36. Romer, C. and M. Ferentinou. 2016. Shallow landslide susceptibility assessment in a semiarid environment-A Quaternary catchment of KwaZulu-Natal, South Africa. Engineering Geology, 201: 29-44. [DOI:10.1016/j.enggeo.2015.12.013]
37. Solaimani, K., S.Z. Mousavi and A. Kavian. 2013. Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6(7): 2557-2569.‌ [DOI:10.1007/s12517-012-0526-5]
38. Steger, S., A. Brenning, R. Bell, H. Petschko and T. Glade. 2016. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology, 262: 8-23.‌ [DOI:10.1016/j.geomorph.2016.03.015]
39. Teimoori Yansari, Z. 2018. The study of landslide susceptibility Chahardange Basin with emphasis on comparative assessment methods. Ph.D. Thesis, Ferdowsi University of Mashhad, Mashhad, Iran, 144 pp (In Persian).
40. Trigila, A., C. Iadanza, C. Esposito and G. Scarascia-Mugnozza. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249: 119-136.‌ [DOI:10.1016/j.geomorph.2015.06.001]
41. Tsangaratos, P., I. Ilia and D. Rozos. 2013. Case Event System for Landslide Susceptibility Analysis. Landslide Science and Practice, 1: 585-593. [DOI:10.1007/978-3-642-31325-7_77]
42. Wang, L. J., M. Guo, K. Sawada, J. Lin and J. Zhang. 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal, 20(1): 117-136.‌ [DOI:10.1007/s12303-015-0026-1]
43. Youssef, A.M., H.R. Pourghasemi, Z.S. Pourtaghi and M.M. Al-Katheeri. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides, 13(5): 839-856. [DOI:10.1007/s10346-015-0614-1]
44. Youssef, A.M., B. Pradhan, H.R. Pourghasemi and S. Abdullahi. 2015. Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS. Geosciences Journal, 19(3): 449-469.‌ [DOI:10.1007/s12303-014-0065-z]
45. Zare, M., A. Moghaddamnia, S. Tali Khoshk, H. Salmani. 2015. Landslide Hazard Assessment by using Neuro-Fuzzy Technique in Vaz Watershed. Watershed Management Research, 11: 101-110 (In Persian).
46. Zhang, G., Y. Cai, Z. Zheng, J. Zhen, Y. Liu and K. Huang. 2016. Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena, 142: 233-244.‌ [DOI:10.1016/j.catena.2016.03.028]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Watershed Management Research

Designed & Developed by : Yektaweb