Abstract
Landslide susceptibility mapping is among the first works for disaster management and land use planning activities in a mountain area like Ganzhou City. The aims of the current study are to assess GIS-based landslide spatial modeling using four models, namely data-driven evidential belief function (EBF), frequency ratio (FR), maximum entropy (Maxent), and logistic regression (LR), and to compare their performances. At first, a landslide inventory map was prepared according to aerial photographs, satellite images, and extensive field surveys. In total, 3971 landslide events were recognized in the study area that used 2979 landslides (75 %) for modeling and 992 landslide events (25 %) for validation. In the next step, the landslide-conditioning factors, namely slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index (TWI), slope-length (LS), lithology, normalized difference vegetation index (NDVI), distance from rivers, distance from faults, distance from roads, and rainfall, were derived from the spatial database. Finally, landslide susceptibility maps of Ganzhou City were mapped in ArcGIS based on EBF, FR, Maxent, and LR approaches and were validated using the receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that in the landslide susceptibility maps using the EBF, FR, Maxent, and LR models, the area under the curve (AUC) values were 0.7367, 0.7789, 0.7903, and 0.8237, respectively. Therefore, it can be concluded that all four models have AUC values of more than 0.70 and can be used in landslide susceptibility mapping in the study area. Also, the LR model had the best performance in the current study. Meanwhile, the mentioned models (EBF, FR, Maxent, and LR) showed almost similar results. The resultant susceptibility maps produced in the current study can be useful for land use planning and hazard mitigation purposes in the study area.
Similar content being viewed by others
References
Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106
Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012a) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34
Akgun A, Kincal C, Pradhan B (2012b) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess 184(9):5453–5470
Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 38(1):23–34. doi:10.1016/j.cageo.2012.03.003
Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36
Bagherzadeh A, Mansouri Daneshvar MR (2012) Mapping of landslide hazard zonation using GIS at Golestan watershed northeast of Iran. Arab J Geosci 6(9):3377–3388
Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraovany-Liptovski Mikulas railway case study. Physics and Chemistry of the Earth, Parts A/B/C 35 (3–5):162–171
Bai SB, Lu GN, Wang J, Zhou PG, Hou SS, Xu SN (2011) GIS-based rare events logistic regression for landslide susceptibility mapping of Lianyungang China. Environ Earth Sci 62:139–149
Bai S B, Wang J, Thiebes B, Cheng C, Chang Z Y (2013) Susceptibility assessments of the Wenchuan earthquake-triggered landslides in Longnan using logistic regression. Environ Earth Sci. doi:10 1007/s12665-013-2475-z
Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study Italy. Math Geosci 44:47–70
Bhandary NP, Dahal RK, Timilsina M, Yatabe R (2013) Rainfall event-based landslide susceptibility zonation mapping. Nat Hazards 69(1):365–388
Bonham-Carter GF (1991) Integration of geoscientific data using GIS. In: Goodchild MF, Rhind DW, Maguire DJ (eds) Geographic information systems: principle and applications. Longman, London, pp 171–184
Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Computer Methamphetamine Geos, vol 13. Pergamon, New York, p 398
Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geol Rev 35:383–400
Carranza EJM, Hale M (2003) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22:117–132
Carranza EJM, Van Ruitenbeek FJA, Hecker C, Van der Meijde M, Van der Meer FD (2008) Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs 10:374–387
Chen SC, Chou HT, Chen SC, Wu CH, Lin BS (2014) Characteristics of rainfall-induced landslides in Miocene formations: a case study of the Shenmu watershed, Central Taiwan. Eng Geol 169:133–146
Cigna F, Bianchini S, Casagli N (2012) How to assess landslide activity and intensity with Persistent Scatterer Interferometry (PSI): the PSI-based matrix approach. Landslides 10(3):267–283
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
Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406
Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012) Factors selection in landslide susceptibility modelling on large scale following the GIS matrix method: application to the river Beiro basin (Spain). Nat Hazard Earth Sys 12(2):327–340
Davoodi Moghaddam D, Rezaei M, Pourghasemi HR, Pourtaghi ZS, Pradhan B (2013) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arab J Geosci. doi:10.1007/s12517-013-1161-5
Del Ventisette C, Garfagnoli F, Ciampalini A, Battistini A, Gigli G, Moretti S, Casagli N (2012) An integrated approach to the study of catastrophic debris-flows: geological hazard and human influence. Nat Hazard Earth Sys 12(9):2907–2922
Demir G, Aytekin M, Akgun A, Ikizler S B, Tatar O (2012) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards doi:10.1007/s11069-012-0418-8
Dempster AP (1968) A generalization of Bayesian inference. J R Stat Soc B 30:205–247
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Althuwaynee OF (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. Nat Hazards 65(1):135–165
Duc DM (2012) Rainfall-triggered large landslides on 15 December 2005 in Van Canh District, Binh Dinh Province, Vietnam. Landslides 10(2):219–230
Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65(3):2105–2128
Felicisimo A, Cuartero A, Remondo J, Quiros E (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
Grelle G, Soriano M, Revellino P, Guerriero L, Anderson M G, Diambra A, Fiorillo F, Esposito L, Diodato N, Guadango F M (2013) Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach optimized for initial water table conditions. Bull Eng Geol Environ. doi:10.1007/s10064-013-0546-8
Hasekiogullari G D, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards. doi:10.1007/s11069-012-0218-1
He S, Pan P, Dai L, Wang H, Liu J (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171–172:30–41
Heshmati M, Arifin A, Shamshuddin J, Majid NM, Ghaituri M (2011) Factors affecting landslides occurrence in agro-ecological zones in the Merek catchment. Iran J Arid Environ 75:1072–1082
Hosmer DW, Lemeshow S (eds) (2000) Applied logistic regression. Wiley InterScience, New York
Huang R, Fan X (2013) The landslide story. Nat Geosci 6(5):325–326
Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Te 11(4):909–926
Jaafari A, Najafi A, Rezaeian J, Sattarian A, Ghajar I (2015) Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy 47:198–208
Karami A, Khoorani A, Noohegar A, Fallah Shamsi SR, Moosavi V (2015) Gully Erosion Mapping Using Object-Based and Pixel-Based Image Classification Methods. Environ & Eng Geosci XXI(2):101–110. doi:10.2113/gseegeosci.21.2.101
Kleinbaum DG, Klein M (2010) Introduction to logistic regression. In: Logistic regression statistics for biology and health. Springer, New York, p 1–39
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41
Lee MJ, Choi JW, Oh HJ, Won JS, Lee S (2012) Ensemble-based landslide susceptibility maps in Jinbu area, Korea. Environ Earth Sci 67(1):23–37
Li XJ, Chen YN, Ouyang H (2002) Analysis on sand disaster with disaster entropy method. Arid Land Geography 25(4):350–353, in Chinese
Mărgărint MC, Grozavu A, Patriche CV (2013) Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression. Nat Hazard Earth Sys Sci 13(12):3339–3355
Ming-Rong L, Guang-Ping C, Xiao-Ming Y (2007) Feature research and preventive countermeasures about the mountain flood and geological disaster in Ganzhou City. J Water Resour Water Eng 18(2):31–35 (in Chinese)
Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio Dempster–Shafer and weights-of-evidence models. J Asian Earth Sci 61:221–236
Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour Res 22:1350–1360
Moore ID, Grayson RB (1991) Terrain-based catchment partitioning and runoff prediction using vector elevation data. Water Resour Res 27(6):1171–1191
Naghibi A, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods for groundwater potential mapping in Iran. Water Resour. Manage 29:5217–5236
Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A (2014) Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Sci Informatics. doi:10.1007/s12145-014-0145-7
Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300
Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20
O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690
Ozdemir A, Altural T (2013) A comparative study of frequency ratio weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 641:80–197
Park NW (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci. 62(2):367–376
Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (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
Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61:1049–1064
Pourghasemi H R, Beheshtirad M (2014) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int. doi: 10.1080/10106049.2014.966161
Pourghasemi H R, Pradhan B, Gokceoglu C (2012a) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed Iran. Nat Hazards. doi:10.1007/s11069-012-0217-2
Pourghasemi H R, Gokceoglu C, Pradhan B, Deylami Moezzi K (2012b) Landslide susceptibility mapping using a spatial multicriteria evaluation model at Haraz Watershed Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49. doi:10.1007/978-3-642-25495-6-2
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012c) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365
Pourghasemi HR, Mohammady M, Pradhan B (2012d) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84
Pourghasemi HR, Pradhan B, Gokceoglu C (2012e) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS, AEROTECH IV–2012. Appl Mech Mater 225:486–491. doi:10.4028/www.scientific.net/AMM.225.486
Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression analytical hierarchy process and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779
Pourtaghi Z S, Pourghasemi H R (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J. doi: 10.1007/s10040-013-1089-6
Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis. Environ Ecol Stat 18(3):471–493
Pradhan B (2012) 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
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Software 25:747–759
Pradhan B, Abokharima M H, Jebur M N, Tehrany M S (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards. doi: 10.1007/s11069-014-1128-1
Raman R, Punia M (2012) The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India. Georisk Assess Manage Risk Eng Syst Geohazards 6(3):145–161
Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187
Regmi A D, Devkota K C, Yoshida K, Pradhan B, Pourghasemi H R, Kumamoto T, Akgun A (2013) 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. doi:10.1007/s12517-012-0807-z
Roering J (2012) Tectonic geomorphology: landslides limit mountain relief. Nat Geosci 5(7):446–447
Sdao F, Lioi DS, Pascale S, Caniani D, Mancini IM (2013) Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat Hazard Earth Sys Sci 13(2):395–407
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton, p 297
Shahabi H, Ahmad B B, Khezri S (2012) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci. doi:10.1007/s12517-012-0650-2
Shirzadi A, Lee S, Oh HJ, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Nat Hazards 64(2):1639–1656
Song KY, Oh HJ, Choi J, Park I, Lee C, Lee S (2012) Prediction of landslide using ASTER imagery and data mining models. Adv Space Res 49:978–993
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick O B (2011) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro fuzzy inference system and GIS. Comput Geosci. doi:10.1016/j cageo.2011.10.031
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines decision tree and naïve Bayes models. Math Probl Eng 1–26
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB et al (2012b) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40
Wei L, Zheng YF, Shan JS (2005) Review on prediction and warning method of landslide hazard triggered by heavy rainfall. Meteorol Mon 31(10):3–6, in Chinese
Wei L, Chen SX, Bian XG (2007) Trial study on factors analysis and prediction of landslide hazard triggered by extreme heavy rainfall. J Appl Meteorol Sci 18(5):682–689 (in Chinese)
Xu C, Xu X, Dai F, Saraf AK (2012) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput Geosci 46:317–329. doi:10.1016/j.cageo.2012.01.002
Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) 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 85(3):274–287
Yi CX, Shi PJ (1994) Entropy production and natural hazard. J Beijing Normal Univ 30(2):276–280 (in Chinese)
Youssef A M, Pourghasemi H R, El-Haddad B A, Dhary B K (2015) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia. Bull Eng Geol Environ. doi:10.1007/s10064-015-0734-9
Yufeng S, Fengxiang J (2009) Landslide stability analysis based on generalized information entropy. Int Conf Environ Sci Inf Appl Technol 2:83–85
Yussef N M, Pradhan B, Shafri H Z M, Jebur M N, Yusoff Z (2015) Spatial landslide hazard assessment along the Jelapang Corridor of the North–south Expressway in Malaysia using high resolution airborne LiDAR data. Arab J Geosci. doi:10.1007/s12517-015-1937-x
Zare M, Pourghasemi H R, Vafakhah M Pradhan B (2012) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi:10.1007/s12517-012-0610-x
Zizioli D, Meisina C, Valentino R, Montrasio L (2013) Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy. Nat Hazard Earth Sys Sci 13(3):559–573
Acknowledgments
The authors would like to thank the two anonymous reviewers and the journal editor for their helpful comments on the previous version of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hong, H., Naghibi, S.A., Pourghasemi, H.R. et al. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9, 112 (2016). https://doi.org/10.1007/s12517-015-2094-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-015-2094-y