Abstract
The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.
Similar content being viewed by others
References
Abella EC, Van Westen C (2007) Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation. Landslides 4:311–325
Abramson LW (2002) Slope stability and stabilization methods. Wiley, New York
Akgün A, Bulut F (2007) GIS-based landslide susceptibility for Arsin–Yomra (Trabzon, North Turkey) region. Environ Geol 51:1377–1387
Akgün A, Türk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region. NW Turk Comput Geosci 37:1515–1524
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
Argyriou AV, Teeuw RM, Rust D, Sarris A (2016) GIS multi-criteria decision analysis for assessment and mapping of neotectonic landscape deformation: a case study from Crete. Geomorphology 253:262–274
Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81
Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany–Liptovský Mikuláš railway case study. Phys Che Earth Parts A/B/C 35:162–171
Bennett ND, Croke BF, Guariso G, Guillaume JH, Hamilton SH, Jakeman AJ, Marsili-Libelli S, Newham LT, Norton JP, Perrin C (2013) Characterising performance of environmental models. Environ Model Softw 40:1–20
Bhandary NP, Dahal RK, Timilsina M, Yatabe R (2013) Rainfall event-based landslide susceptibility zonation mapping. Nat Hazards 69:365–388
Bijukchhen SM, Kayastha P, Dhital MR (2013) A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal. Arab J Geosci 6:2727–2743
Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862
Budimir M, Atkinson P, Lewis H (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12:419–436
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211
Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2015a) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomat Nat Hazards Risk 6:243–271
Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015b) 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 13:1–18
Calò F, Ardizzone F, Castaldo R, Lollino P, Tizzani P, Guzzetti F, Lanari R, Angeli M-G, Pontoni F, Manunta M (2014) Enhanced landslide investigations through advanced DInSAR techniques: the Ivancich case study, Assisi, Italy. Remote Sens Environ 142:69–82
Calvello M, Papa MN, Pratschke J, Crescenzo MN (2015) Landslide risk perception: a case study in Southern Italy. Landslides 13:1–12
Chen W, Panahi M, Pourghasemi HR (2017a) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324
Chen W, Pourghasemi HR, Naghibi SA (2017b) A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bull Eng Geol Environ 77:1–18. https://doi.org/10.1007/s10064-017-1010-y
Chen W, Pourghasemi HR, Naghibi SA (2017c) Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bull Eng Geol Environ 77:1–19. https://doi.org/10.1007/s10064-017-1004-9
Chen W, Pourghasemi HR, Zhao Z (2017d) A GIS-based comparative study of Dempster–Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int 32:367–385. https://doi.org/10.1080/10106049.2016.1140824
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017e) 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
Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Zhu A-X, Pei X, Duan Z (2018a) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135
Chen W, Xie X, Peng J, Shahabi H, Hong H, Bui DT, Duan Z, Li S, Zhu A-X (2018b) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA 164:135–149
Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Li W, Pan D, Hui J, Ma M (2018c) A novel ensemble approach of bivariate statistical based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int. https://doi.org/10.1080/10106049.2018.1425738
Chowdhury R, Bertoldi C (1977) Residual shear tests on soil from two natural slopes. Aust Geomech J G 7:1–9
Ciampalini A, Raspini F, Bianchini S, Frodella W, Bardi F, Lagomarsino D, Di Traglia F, Moretti S, Proietti C, Pagliara P (2015) Remote sensing as tool for development of landslide databases: the case of the Messina Province (Italy) geodatabase. Geomorphology 249:103–118
Comino JR, Iserloh T, Lassu T, Cerdà A, Keestra S, Prosdocimi M, Brings C, Marzen M, Ramos M, Senciales J (2016) Quantitative comparison of initial soil erosion processes and runoff generation in Spanish and German vineyards. Sci Total Environ 565:1165–1174
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
Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 11:639–653
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510
D’Arco M, Liccardo A, Pasquino N (2012) ANOVA-based approach for DAC diagnostics. IEEE Trans Instrum Meas 61:1874–1882
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, 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 65:135–165. https://doi.org/10.1007/s11069-012-0347-6
Di Martire D, Tessitore S, Brancato D, Ciminelli MG, Costabile S, Costantini M, Graziano GV, Minati F, Ramondini M, Calcaterra D (2016) Landslide detection integrated system (LaDIS) based on in situ and satellite SAR interferometry measurements. CATENA 137:406–421
Dong J-J, Tung Y-H, Chen C-C, Liao J-J, Pan Y-W (2009) Discriminant analysis of the geomorphic characteristics and stability of landslide dams. Geomorphology 110:162–171
Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189
Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675–701
Fu KL, Lin BS, Thomas K, Chen CK, Ho HC (2016) Evaluation of environmental factors in landslide prone areas of central Taiwan using spatial analysis of landslide inventory maps. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-2016-127
Gaprindashvili G, Van Westen CJ (2016) Generation of a national landslide hazard and risk map for the country of Georgia. Nat Hazards 80:69–101
Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85
Guo C, Montgomery DR, Zhang Y, Wang K, Yang Z (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110
Hong Y, Adler R, Huffman G (2007) Use of satellite remote sensing data in the mapping of global landslide susceptibility. Nat Hazards 43:245–256
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA 133:266–281
Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9:1–26
Hong H, Liu J, Zhu A-X, Shahabi H, Pham BT, Chen W, Pradhan B, Bui DT (2017) A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). Environ Earth Sci 76:652
Ipe D (1987) Performing the Friedman test and the associated multiple comparison test using PROC GLM. In: Proceedings of the twelfth annual SAS users group international conference, pp 1146–1148
Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11:352–359
Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28
Lombardo L, Cama M, Conoscenti C, Märker M, Rotigliano E (2015) Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy). Nat Hazards 79:1621–1648
Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, New York
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
Palenzuela J, Jiménez-Perálvarez J, El Hamdouni R, Alameda-Hernández P, Chacón J, Irigaray C (2015) Integration of LiDAR data for the assessment of activity in diachronic landslides: a case study in the Betic Cordillera (Spain). Landslides 13:1–14
Parras-Alcántara L, Lozano-García B, Keesstra S, Cerdà A, Brevik EC (2016) Long-term effects of soil management on ecosystem services and soil loss estimation in olive grove top soils. Sci Total Environ 571:498–506
Petley D (2010) On the impact of climate change and population growth on the occurrence of fatal landslides in South, East and SE Asia. Q J Eng Geol Hydrogeol 43:487–496
Pham BT, Bui DT, Dholakia M, Prakash I, Pham HV (2016a) 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
Pham BT, Bui DT, Prakash I, Dholakia M (2016b) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards 83:97–127
Pham BT, Tien Bui D, Prakash I, Dholakia M (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63
Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2012a) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49
Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996
Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122:349–369
Pourghasemi H, Moradi H, Aghda SF, Gokceoglu C, Pradhan B (2014) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7:1857–1878
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
Pradhan A, Kim Y (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. CATENA 140:125–139
Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48:4164–4177
Quan-min X, Xiang B, Yuan-you X (2005) Systematic analysis of risk evaluation of landslide hazard. Rock Soil Mech 26:71–74
Rahman R, Saha S (2008) Remote sensing, spatial multi criteria evaluation (SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern planning for a flood prone area. J Spat Sci 53:161–177
Randles RH (1988) Wilcoxon signed rank test. Encyclopedia of statistical sciences. Wiley Online Library, Hoboken, NJ
Raska P, Klimes J, Dubisar J (2015) Using local archive sources to reconstruct historical landslide occurrence in selected urban regions of the Czech Republic: examples from regions with different historical development. Land Degrad Dev 26:142–157
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281
Sahnoun H, Serbaji MM, Karray B, Medhioub K (2012) GIS and multi-criteria analysis to select potential sites of agro-industrial complex. Environ Earth Sci 66:2477–2489
San BT (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: the Candir catchment area (western Antalya, Turkey). Int J Appl Earth Obs Geoinf 26:399–412
Sassa K, Canuti P (2008) Landslides-disaster risk reduction. Springer, Berlin
Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899
Shahabi H, Ahmad B, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci 6:3885–3907
Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70
Shahabi H, Hashim M, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environ Earth Sci 73:8647–8668
Shirzadi A, Saro L, Joo OH, 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 64:1639–1656
Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I (2017a) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76:60
Shirzadi A, Shahabi H, Chapi K, Bui DT, Pham BT, Shahedi K, Ahmad BB (2017b) A comparative study between popular statistical and machine learning methods for simulating volume of landslides. CATENA 157:213–226
Symeonakis E, Karathanasis N, Koukoulas S, Panagopoulos G (2016) Monitoring sensitivity to land degradation and desertification with the environmentally sensitive area index: the case of lesvos island. Land Degrad Dev 27:1562–1573
Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using Support vector machines, Decision tree and Naïve Bayes models. Math Probl Eng. https://doi.org/10.1155/2012/974638
Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) 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
Tsai F, Lai J-S, Chen WW, Lin T-H (2013) Analysis of topographic and vegetative factors with data mining for landslide verification. Ecol Eng 61:669–677
Vapnik V (2013) The nature of statistical learning theory. Springer, Berlin
Wang L-J, Sawada K, Moriguchi S (2013) Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Comput Geosci 57:81–92
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83
Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31:2097–2110
Wu X, Ren F, Niu R (2014) Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China. Environ Earth Sci 71:4725–4738
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80
Yalcin A, Reis S, Aydinoglu A, 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 85:274–287
Zhang S, Zhang L, Lacasse S, Nadim F (2016) Evolution of mass movements near epicentre of Wenchuan earthquake, the first eight years. Sci Rep 6:36154
Acknowledgements
The authors would like to thank editor and anonymous reviewers for their meaningful comments on the primary version of the manuscript. This research was supported by the Universiti Teknologi Malaysia (UTM) based on Research University Grant (Q.J130000.2527.17H84).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hong, H., Shahabi, H., Shirzadi, A. et al. Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards 96, 173–212 (2019). https://doi.org/10.1007/s11069-018-3536-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-018-3536-0