Abstract
The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.
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References
Aghdam IN, Morshed Varzandeh MH, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75:553. https://doi.org/10.1007/s12665-015-5233-6
Alkhasawneh MS, Ngah UK, Tay LT, Mat Isa NA, Al-Batah MS (2014) Modeling and testing landslide hazard using decision tree. J Appl Math 2014: 9 https://doi.org/10.1155/2014/929768
Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble decision tree-based Chi-squared automatic interaction detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping. Landslides 11:1063–1078. https://doi.org/10.1007/s10346-014-0466-0
Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37(5):1190–1209. https://doi.org/10.1080/01431161.2016.1148282
Ayalew L, Yamagishi H (2005) The application of GIS–based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, central Japan. Geomorphology 65:15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Baker S, Cousins RD (1984) Clarification of the use of CHI-square and likelihood functions in fits to histograms. Nucl Instrum Methods Phys Res 221:437–442. https://doi.org/10.1016/0167-5087(84)90016-4
Biggs D, Barry DV, Suen E (1991) A method of choosing multi way partitions for classification and decision trees. J Appl Stat 18:49–62. https://doi.org/10.1080/02664769100000005
Chen W, Xie X, Wang J, Pradhan B, Honh H, Tien Bui D, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250. https://doi.org/10.1016/j.catena.2013.08.006
Daum (2018) http://map.daum.net. 21 Mar 2018
Feizizadeh B, Shadman RM, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221. https://doi.org/10.1016/j.cageo.2014.08.001
Gariano SL, Rianna G, Petrucci O, Guzzetti F (2017) Assessing future changes in the occurrence of rainfall-induced landslides at a regional scale. Sci Total Environ 596–597:417–426. https://doi.org/10.1016/j.scitotenv.2017.03.103
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007 2015.
Grozavu A, Plescan S, Patriche CV, Margarint MC, Rosca B (2013) Landslide susceptibility assessment: GIS application to a complex mountainous environment. The Carpathians: Integrating NATURE and Society towards Sustainability. Environ Sci Eng 31–44. https://doi.org/10.1007/978-3-642-12725-0_4
Guisan A, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modeling of plant species distribution. Plant Ecol 143:107–122. https://doi.org/10.1023/A:1009841519580
Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016a) GIS-based landslide spatial modeling in Ganzhou city, China. Arab J Geosci 9(2):1–26. https://doi.org/10.1007/s12517-015-2094-y
Hong H, Pourghasemi HR, Pourtaghi ZS (2016b) 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. https://doi.org/10.1016/j.geomorph.2016.02.012
Hosmer DW, Lemeshow S (2000) Applied logistic regression. second edn. Wiley, New York
Iwahashi J, Pike RJ (2007) Automated classifications of topography from dams by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology 86:409–440. https://doi.org/10.1016/j.geomorph.2006.09.012
Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119–127. https://doi.org/10.2307/2986296
Landsat imagery (2018). https://earthexplorer.usgs.gov. 21 July 2018
Lee S, Lee MJ (2006) Detecting landslide location using KOMPSAT 1 and its application to landslide susceptibility mapping at the Gangneung area, Korea. Adv Space Res 38:2261–2271. https://doi.org/10.1016/j.asr.2006.03.036
Lee S, Lee MJ (2017) Susceptibility mapping of Umyeonsan using logistic regression (LR) model and post-validation through field investigation. Korean J Remote Sens 33:1047–1060. https://doi.org/10.7780/kjrs.2017.33.6.2.2
Lee S, Park I (2013) Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J Environ Manage 127:166–176. https://doi.org/10.1016/j.jenvman.2013.04.010
Lee S, Song KY, Oh HJ, Choi J (2012) Detection of landslide using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis. Int J Remote Sens 33:4937–4966. https://doi.org/10.1080/01431161.2011.649862
Lee MJ, Park I, Lee S (2015) Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea. Environ Earth Sci 74(1):413–429. https://doi.org/10.1007/s12665-015-4048-9
Lee MJ, Park I, Won JS, Lee S (2016) Landslide hazard mapping considering rainfall probability in Inje, Korea. Geomatics. Nat Hazards Risk 7(1):424–446. https://doi.org/10.1080/19475705.2014.931307
Lee S, Hong S, Jung H (2017) A support vector machine for landslide susceptibility mapping in Gangwon province. Korea Sustain 9(1):48. https://doi.org/10.3390/su9010048
Lin GF, Chang MJ, Huang YC, Ho JY (2017) Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Eng Geol 224:62–74. https://doi.org/10.1016/j.enggeo.2017.05.009
Loh WY, Shih YS (1997) Split selection methods for classification trees. Statistica Sinica 7:815–840. http://www.jstor.org/stable/24306157
Mezaal MR, Pradhan B (2018) Data mining-aided automatic landslide detection airborne laser scanning data in densely forested tropical areas. Korean J Remote Sens 34:45–74. https://doi.org/10.7780/kjrs.2018.34.1.4
Montz BE, Tobin GA, Hagelman RR (2017) Natural hazards: explanation and integration. Guilford Publications, New York
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30. https://doi.org/10.1002/hyp.3360050103
Oh HJ (2010) Landslide detection and landslide susceptibility mapping using aerial photos and artificial neural network. Korean J Remote Sens 26:47–57
Oh CY, Kim KT, Chou CU (2009) Analysis of landslide characteristics of Inje area using SPOT5 image and GIS analysis. Korean J Remote Sens 25:445–454
Park NW, Kyriakidis PC (2008) Gestatistical integration of different sources of elevation and its effect on landslide hazard mapping. Korean J Remote Sens 24:453–462
Park I, Lee J, Lee S (2014) Ensemble of ground subsidence hazard maps using fuzzy logic. Central Eur J Geosci 6(2):207–218. https://doi.org/10.2478/s13533-012-0175-y
Peng L, Niu RQ, Huang B, Wu XL, Zhao YN, Ye RQ (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the Three Gorges area, China. Geomorphology 204:287–301. https://doi.org/10.1016/j.geomorph.2013.08.013
Pham BT, Tien Bui D, Dholakia MB, Prakash I, Pham HV (2016) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng 34:1807–1824. https://doi.org/10.1007/s10706-016-9990-0
Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38:301–320. https://doi.org/10.1007/s12524-010-0020-z
Pradhan B (2011) 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 18:471–493. https://doi.org/10.1007/s10651-010-0147-7
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. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
Samodra G, Chen G, Sartohadi J, Kasama K (2017) Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping. Environ Earth Sci 76:184. https://doi.org/10.1007/s12665-017-6475-2
Schmidt KM, Roering JJ, Stock JD, Dietrich WE, Montgomery DR, Schaub T (2001) The variability of root cohesion as an influence on shallow landslide susceptibility in the Oregon Coast Range. Can Geotech J 38:995–1024. https://doi.org/10.1139/t01-031
Schwarz M, Preti F, Giadrossich F, Lehmann P, Or D (2010) Quantifying the role of vegetation in slope stability: a case study in Tuscany (Italy). Ecol Eng 36:285–291. https://doi.org/10.1016/j.ecoleng.2009.06.014
Steger S, Brenning A, Bell R, Petschko H, Glade T (2016) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23. https://doi.org/10.1016/j.geomorph.2016.03.015
Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Tien Bui D, Tuan TA, Hoang ND, Thanh NQ, Nguyen DB, Liem NV, 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 14:447–458. https://doi.org/10.1007/s10346-016-0711-9
Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through an artificial neural network classifier. Nat Hazards 74(3):1489–1516. https://doi.org/10.1007/s11069-014-1245-x
Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection. Greece Landslides 13(2):305–320. https://doi.org/10.1007/s10346-015-0565-6
Van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation why is it still so difficult. Bull Eng Geol Env 65:167–184. https://doi.org/10.1007/s10064-005-0023-0
Wang LJ, Guo M, Sawada K, Lin J, Zhang J (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J 20:117–136. https://doi.org/10.1007/s12303-015-0026-1
Acknowledgements
This research was part of a Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science, ICT, and supported by two National Research Foundation of Korea (NRF)-grants from the Korean government (MSIP) (Nos. 2015M1A3A3A02013416 and 2017R1A2B4003258), and the Korea Meteorological Administration Research and Development Program (Grant No. KMIPA 2015-3071). Also, the study was supported by a 2017 Research Grant from Kangwon National University.
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Kadavi, P.R., Lee, CW. & Lee, S. Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environ Earth Sci 78, 116 (2019). https://doi.org/10.1007/s12665-019-8119-1
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DOI: https://doi.org/10.1007/s12665-019-8119-1