Skip to main content

Advertisement

SpringerLink
Log in

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abolmasov B, Milenkovic S, Marjanovic M, Duric U, Jelisavac B (2015) A geotechnical model of the Umka landslide with reference to landslides in weathered Neogene marls in Serbia. Landslides 12:689–702. doi:10.1007/s10346-014-0499-4

    Article  Google Scholar 

  • Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095. doi:10.1007/s10346-014-0521-x

    Article  Google Scholar 

  • Akcay O (2015) Landslide fissure inference assessment by ANFIS and logistic regression using UAS-based photogrammetry. ISPRS Int J Geo-Inf 4:2131–2158. doi:10.3390/ijgi4042131

    Article  Google Scholar 

  • Alvioli M, Baum RL (2016) Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface. Environ Model Softw 81:122–135. doi:10.1016/j.envsoft.2016.04.002

    Article  Google Scholar 

  • Andrews DW (1988) Chi square diagnostic tests for econometric models: introduction and applications. J Econ 37:135–156

    Article  Google Scholar 

  • Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and Head/tail breaks in LSI (Landslide Susceptibility Index) classification for landslide susceptibility mapping: a case study in Ponorogo, East Java, Indonesia. International Conference on Science in Information Technology, Yogyakarta, Indonesia, pp 337–342

  • Bellugi D, Milledge DG, Dietrich WE, McKean JA, Perron JT, Sudderth EB, Kazian B (2015) A spectral clustering search algorithm for predicting shallow landslide size and location. J Geophys Res Earth Surf 120:300–324. doi:10.1002/2014jf003137

    Article  Google Scholar 

  • Bennett GL, Miller SR, Roering JJ, Schmidt DA (2016) Landslides, threshold slopes, and the survival of relict terrain in the wake of the Mendocino Triple Junction. Geology 44:363–366. doi:10.1130/g37530.1

    Article  Google Scholar 

  • Benoit L, Briole P, Martin O, Thom C, Malet JP, Ulrich P (2015) Monitoring landslide displacements with the Geocube wireless network of low-cost GPS. Eng Geol 195:111–121. doi:10.1016/j.enggeo.2015.05.020

    Article  Google Scholar 

  • Bertoni A, Folgieri R, Valentini G (2005) Bio-molecular cancer prediction with random subspace ensembles of support vector machines. Neurocomputing 63:535–539

    Article  Google Scholar 

  • Bezak N, Šraj M, Mikoš M (2016) Copula-based IDF curves and empirical rainfall thresholds for flash floods and rainfall-induced landslides. J Hydrol. doi:10.1016/j.jhydrol.2016.02.058

    Google Scholar 

  • Blothe JH, Korup O, Schwanghart W (2015) Large landslides lie low: excess topography in the Himalaya–Karakoram ranges. Geology 43:523–526. doi:10.1130/g36527.1

    Article  Google Scholar 

  • Boue A, Lesage P, Cortes G, Valette B, Reyes-Davila G (2015) Real-time eruption forecasting using the material failure forecast method with a Bayesian approach. J Geophys Res Solid Earth 120:2143–2161. doi:10.1002/2014jb011637

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bui DT, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2015) 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. doi:10.1080/19475705.2013.843206

    Article  Google Scholar 

  • Bui DT, 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. doi:10.1007/s10346-015-0557-6

    Article  Google Scholar 

  • Bui DT, Nguyen QP, Hoang ND, Klempe H (2017) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14(1):1–17. doi:10.1007/s10346-016-0708-4

    Article  Google Scholar 

  • Carey JM, Moore R, Petley DN (2015) Patterns of movement in the Ventnor landslide complex, Isle of Wight, southern England. Landslides 12:1107–1118. doi:10.1007/s10346-014-0538-1

    Article  Google Scholar 

  • Carlini M, Chelli A, Vescovi P, Artoni A, Clemenzi L, Tellini C, Torelli L (2016) Tectonic control on the development and distribution of large landslides in the Northern Apennines (Italy). Geomorphology 253:425–437. doi:10.1016/j.geomorph.2015.10.028

    Article  Google Scholar 

  • Chen G, Zhao KG, McDermid GJ, Hay GJ (2012) The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. Int J Remote Sens 33:2909–2924. doi:10.1080/01431161.2011.624130

    Article  Google Scholar 

  • Chen W, Chai H, Zhao Z, Wang Q, Hong H (2016a) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environ Earth Sci 75:474

  • Chen W, Wang J, Xie X, Hong H, Trung NV, Tien Bui D, Wang G, Li X (2016b) Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci 75:1344

  • 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

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017b) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327. doi:10.1016/j.geoderma.2017.06.020

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Naghibi SA (2017c) 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. doi:10.1007/s10064-017-1010-y

    Google Scholar 

  • Chen W, Pourghasemi HR, Naghibi SA (2017d) Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bull Eng Geol Environ. doi:10.1007/s10064-017-1004-9

    Google Scholar 

  • Chen W, Pourghasemi HR, Zhao Z (2017e) A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto Int 32:367–385. doi:10.1080/10106049.2016.1140824

    Article  Google Scholar 

  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017f) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Hazards Risk. doi:10.1080/19475705.2017.1289250

    Google Scholar 

  • Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017g) 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

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017h) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85. doi:10.1016/j.geomorph.2017.09.007

  • Chou H-T, Lee C-F, Lo C-M (2016) The formation and evolution of a coastal alluvial fan in eastern Taiwan caused by rainfall-induced landslides. Landslides. doi:10.1007/s10346-016-0678-6

    Google Scholar 

  • Ciabatta L, Brocca L, Massari C, Moramarco T, Puca S, Rinollo A, Gabellani S, Wagner W (2015) Integration of satellite soil moisture and rainfall observations over the italian territory. J Hydrometeorol 16:1341–1355. doi:10.1175/jhm-d-14-0108.1

    Article  Google Scholar 

  • Ciurleo M, Calvello M, Cascini L (2016) Susceptibility zoning of shallow landslides in fine grained soils by statistical methods. CATENA 139:250–264. doi:10.1016/j.catena.2015.12.017

    Article  Google Scholar 

  • Clare MA, Talling PJ, Hunt JE (2015) Implications of reduced turbidity current and landslide activity for the initial eocene thermal maximum: evidence from two distal, deep-water sites. Earth Planet Sci Lett 420:102–115. doi:10.1016/j.epsl.2015.03.022

    Article  Google Scholar 

  • Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gomez-Gutierrez A, Rotigliano E, Agnesi V (2015) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Bence River basin (western Sicily, Italy). Geomorphology 242:49–64. doi:10.1016/j.geomorph.2014.09.020

    Article  Google Scholar 

  • Conte E, Donato A, Troncone A (2016) A simplified method for predicting rainfall-induced mobility of active landslides. Landslides. doi:10.1007/s10346-016-0692-8

    Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228. doi:10.1016/S0169-555X(01)00087-3

    Article  Google Scholar 

  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWAM) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122–148. doi:10.1016/j.catena.2015.07.020

    Article  Google Scholar 

  • Dickson ME, Perry GLW (2016) Identifying the controls on coastal cliff landslides using machine-learning approaches. Environ Model Softw 76:117–127. doi:10.1016/j.envsoft.2015.10.029

    Article  Google Scholar 

  • Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776

    Article  Google Scholar 

  • Fan L, Lehmann P, Or D (2016) Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides. Water Resour Res 52:1781–1799. doi:10.1002/2015WR017758

    Article  Google Scholar 

  • Feng Z-Y, Lo C-M, Lin Q-F (2016) The characteristics of the seismic signals induced by landslides using a coupling of discrete element and finite difference methods. Landslides. doi:10.1007/s10346-016-0714-6

    Google Scholar 

  • 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. doi:10.1007/s11069-015-1958-5

    Article  Google Scholar 

  • 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. doi:10.1016/j.cageo.2015.04.007

    Article  Google Scholar 

  • Gullà G, Peduto D, Borrelli L, Antronico L, Fornaro G (2016) Geometric and kinematic characterization of landslides affecting urban areas: the Lungro case study (Calabria, Southern Italy). Landslides 4:1–18. doi:10.1007/s10346-015-0676-0

    Google Scholar 

  • Gutiérrez F, Lizaga I (2016) Sinkholes, collapse structures and large landslides in an active salt dome submerged by a reservoir: the unique case of the Ambal ridge in the Karun River, Zagros Mountains, Iran. Geomorphology 254:88–103. doi:10.1016/j.geomorph.2015.11.020

    Article  Google Scholar 

  • Hadayeghi A, Shalaby AS, Persaud BN (2010) Development of planning level transportation safety tools using geographically weighted poisson regression. Accid Anal Prev 42:676–688. doi:10.1016/j.aap.2009.10.016

    Article  Google Scholar 

  • Haeberli W, Schaub Y, Huggel C (2001) Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges. Geomorphology. doi:10.1016/j.geomorph.2016.02.009

    Google Scholar 

  • Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Math Geosci 42:657–680. doi:10.1007/s11004-010-9284-7

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hong H, Pourghasemi HR, Pourtaghi ZS (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

    Article  Google Scholar 

  • Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Tien Bui D (2017) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 32:139–154. doi:10.1080/10106049.2015.1130086

    Google Scholar 

  • Iadanza C, Trigila A, Napolitano F (2016) Identification and characterization of rainfall events responsible for triggering of debris flows and shallow landslides. J Hydrol. doi:10.1016/j.jhydrol.2016.01.018

    Google Scholar 

  • Kirschbaum D, Stanley T, Zhou YP (2015) Spatial and temporal analysis of a global landslide catalog. Geomorphology 249:4–15. doi:10.1016/j.geomorph.2015.03.016

    Article  Google Scholar 

  • Kotsiantis S (2011) Combining bagging, boosting, rotation forest and random subspace methods. Artif Intell Rev 35:223–240

    Article  Google Scholar 

  • Koutsias N, Martinez-Fernandez J, Allgower B (2010) Do factors causing wildfires vary in space? Evidence from geographically weighted regression. Gisci Remote Sens 47:221–240. doi:10.2747/1548-1603.47.2.221

    Article  Google Scholar 

  • Kritikos T, Davies T (2015) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand. Landslides 12:1051–1075. doi:10.1007/s10346-014-0533-6

    Article  Google Scholar 

  • Kumar S, Lal R, Liu DS (2012) A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189:627–634. doi:10.1016/j.geoderma.2012.05.022

    Article  Google Scholar 

  • Kuncheva LI, Plumpton CO (2010) Choosing parameters for random subspace ensembles for fMRI classification. In: El Gayar N, Kittler J, Roli F (eds) Multiple classifier systems: 9th international workshop, MCS 2010, Cairo, Egypt, 7–9 April 2010 Proceedings. Springer, Berlin, pp 54–63

    Chapter  Google Scholar 

  • Kuncheva LI, Rodríguez JJ, Plumpton CO, Linden DE, Johnston SJ (2010) Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging 29:531–542

    Article  Google Scholar 

  • LaHusen SR, Duvall AR, Booth AM, Montgomery DR (2016) Surface roughness dating of long-runout landslides near Oso, Washington (USA), reveals persistent postglacial hillslope instability. Geology 44:111–114. doi:10.1130/g37267.1

    Article  Google Scholar 

  • Lai C, Reinders MJT, Wessels L (2006) Random subspace method for multivariate feature selection. Pattern Recogn Lett 27:1067–1076. doi:10.1016/j.patrec.2005.12.018

    Article  Google Scholar 

  • Leung Y, Mei C-L, Zhang W-X (2000) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environ Plan A 32:871–890

    Article  Google Scholar 

  • Li G, West AJ, Densmore AL, Hammond DE, Jin Z, Zhang F, Wang J, Hilton RG (2016) Connectivity of earthquake-triggered landslides with the fluvial network: implications for landslide sediment transport after the 2008 Wenchuan earthquake. J Geophys Res Earth Surf 121:703–724

  • Lin CH, Jan JC, Pu HC, Tu Y, Chen CC, Wu YM (2015) Landslide seismic magnitude. Earth Planet Sci Lett 429:122–127

    Article  Google Scholar 

  • Lora M, Camporese M, Troch PA, Salandin P (2016) Rainfall-triggered shallow landslides: infiltration dynamics in a physical hillslope model. Hydrol Process. doi:10.1002/hyp.10829

    Google Scholar 

  • Lukawska-Matuszewska K, Urbanski JA (2014) Prediction of near-bottom water salinity in the Baltic Sea using ordinary least squares and geographically weighted regression models. Estuar Coast Shelf Sci 149:255–263. doi:10.1016/j.ecss.2014.09.003

    Article  Google Scholar 

  • Ma H-R, Cheng X, Chen L, Zhang H, Xiong H (2016) Automatic identification of shallow landslides based on Worldview2 remote sensing images. APPRES 10:016008–016008. doi:10.1117/1.JRS.10.016008

    Article  Google Scholar 

  • Martinez-Fernandez J, Chuvieco E, Koutsias N (2013) Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Syst Sci 13:311–327. doi:10.5194/nhess-13-311-2013

    Article  Google Scholar 

  • Meinhardt M, Fink M, Tunschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234:80–97. doi:10.1016/j.geomorph.2014.12.042

    Article  Google Scholar 

  • Mertens K, Jacobs L, Maes J, Kabaseke C, Maertens M, Poesen J, Kervyn M, Vranken L (2016) The direct impact of landslides on household income in tropical regions: a case study from the Rwenzori Mountains in Uganda. Sci Total Environ 550:1032–1043. doi:10.1016/j.scitotenv.2016.01.171

    Article  Google Scholar 

  • Mielniczuk J, Teisseyre P (2014) Using random subspace method for prediction and variable importance assessment in linear regression. Comput Stat Data Anal 71:725–742. doi:10.1016/j.csda.2012.09.018

    Article  Google Scholar 

  • Moore ZT, Sawyer DE (2016) Assessing post-failure mobility of submarine landslides from seismic geomorphology and physical properties of mass transport deposits: an example from seaward of the Kumano Basin, Nankai Trough, offshore Japan. Mar Geol 374:73–84. doi:10.1016/j.margeo.2016.02.003

    Article  Google Scholar 

  • Nanni L, Lumini A (2008) Random subspace for an improved biohashing for face authentication. Pattern Recogn Lett 29:295–300

    Article  Google Scholar 

  • Nasiri Aghdam I, Varzandeh MHM, 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 75:1–20

    Article  Google Scholar 

  • Nicolussi K, Spotl C, Thurner A, Reimer PJ (2015) Precise radiocarbon dating of the giant Weis landslide (Eastern Alps, Austria). Geomorphology 243:87–91. doi:10.1016/j.geomorph.2016.05.001

    Article  Google Scholar 

  • Ogneva-Himmelberger Y, Pearsall H, Rakshit R (2009) Concrete evidence and geographically weighted regression: a regional analysis of wealth and the land cover in Massachusetts. Appl Geogr 29:478–487

    Article  Google Scholar 

  • Oliveira SC, Zezere JL, Catalao J, Nico G (2015) The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas. Landslides 12:703–719. doi:10.1007/s10346-014-0522-9

    Article  Google Scholar 

  • Osadchiev AA, Korotenko KA, Zavialov PO, Chiang WS, Liu CC (2016) Transport and bottom accumulation of fine river sediments under typhoon conditions and associated submarine landslides: case study of the Peinan River, Taiwan. Nat Hazards Earth Syst Sci 16:41–54. doi:10.5194/nhess-16-41-2016

    Article  Google Scholar 

  • Paez A, Farber S, Wheeler D (2011) A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships. Environ Plan A 43:2992–3010. doi:10.1068/a44111

    Article  Google Scholar 

  • Palamakumbure D, Flentje P, Stirling D (2015) Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Comput Geosci 82:13–22. doi:10.1016/j.cageo.2015.05.002

    Article  Google Scholar 

  • Panek T (2015) Recent progress in landslide dating: a global overview. Prog Phys Geogr 39:168–198. doi:10.1177/0309133314550671

    Article  Google Scholar 

  • Paulin GL, Bursik MI, Orozco JJZ, Garcia JEF (2015) Landslide susceptibility of volcanic landforms in the Rio El Estado watershed, Pico de Orizaba volcano, Mexico. Nat Hazards 77:559–574. doi:10.1007/s11069-015-1612-2

    Article  Google Scholar 

  • Peng L, Xu SN, Hou JW, Peng JH (2015) Quantitative risk analysis for landslides: the case of the Three Gorges area, China. Landslides 12:943–960. doi:10.1007/s10346-014-0518-5

    Article  Google Scholar 

  • Pham BT, Bui DT, Indra P, Dholakia MB (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method. Int J Eng Res Technol 4:338–344. doi:10.17577/IJERTV4IS110285

    Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia M (2016a) Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS. J Geomat 10:71–79

    Google Scholar 

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia M (2016b) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia MB (2016c) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat Hazards 83:1–31. doi:10.1007/s11069-016-2304-2

    Article  Google Scholar 

  • Piao Y, Piao M, Jin CH, Shon HS, Chung J-M, Hwang B, Ryu KH (2015) A new ensemble method with feature space partitioning for high-dimensional data classification. Math Probl Eng 2015:590678. doi:10.1155/2015/590678

  • Pirdavani A, Bellemans T, Brijs T, Kochan B, Wets G (2014a) Assessing the road safety impacts of a teleworking policy by means of geographically weighted regression method. J Transp Geogr 39:96–110. doi:10.1016/j.jtrangeo.2014.06.021

    Article  Google Scholar 

  • Pirdavani A, Bellemans T, Brijs T, Wets G (2014b) Application of geographically weighted regression technique in spatial analysis of fatal and injury crashes. J Transp Eng. doi:10.1061/(asce)te.1943-5436.0000680

    Google Scholar 

  • Posner AJ, Georgakakos KP (2015) Normalized landslide index method for susceptibility map development in El Salvador. Nat Hazards 79:1825–1845. doi:10.1007/s11069-015-1930-4

    Article  Google Scholar 

  • 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. Comp Geosci 51:350–365

  • Promper C, Glade T (2016) Multilayer-exposure maps as a basis for a regional vulnerability assessment for landslides: applied in Waidhofen/Ybbs, Austria. Nat Hazards 82:111–127. doi:10.1007/s11069-016-2311-3

    Article  Google Scholar 

  • Rao J, Scott A (1987) On simple adjustments to Chi-square tests with sample survey data. Ann Stat 15:385–397

  • Romano A, Di Risio M, Bellotti G, Molfetta MG, Damiani L, De Girolamo P (2016) Tsunamis generated by landslides at the coast of conical islands: experimental benchmark dataset for mathematical model validation. Landslides. doi:10.1007/s10346-016-0696-4

    Google Scholar 

  • Satorra A, Bentler PM (2001) A scaled difference Chi square test statistic for moment structure analysis. Psychometrika 66:507–514

    Article  Google Scholar 

  • Sewell RJ, Parry S, Millis SW, Wang N, Rieser U, DeWitt R (2015) Dating of debris flow fan complexes from Lantau Island, Hong Kong, China: the potential relationship between landslide activity and climate change. Geomorphology 248:205–227. doi:10.1016/j.geomorph.2015.07.041

    Article  Google Scholar 

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep. doi:10.1038/srep09899

    Google Scholar 

  • Sharma V, Irmak S, Kabenge I (2011) Application of GIS and geographically weighted regression to evaluate the spatial non-stationarity relationships between precipitation vs. irrigated and rainfed maize and soybean yields. Trans Asabe 54:953–972

    Article  Google Scholar 

  • Shi JS, Wu LZ, Wu SR, Li B, Wang T, Xin P (2016) Analysis of the causes of large-scale loess landslides in Baoji, China. Geomorphology 264:109–117. doi:10.1016/j.geomorph.2016.04.013

    Article  Google Scholar 

  • Skurichina M, Duin RP (2001) Bagging and the random subspace method for redundant feature spaces. Multiple classifier systems. Springer, Berlin, pp 1–10

    Google Scholar 

  • Skurichina M, Duin RP (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–135

    Article  Google Scholar 

  • Song WZ, Jia HF, Huang JF, Zhang YY (2014) A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China. Remote Sens Environ 154:1–7. doi:10.1016/j.rse.2014.08.008

    Article  Google Scholar 

  • Sun S, Zhang C (2007) The selective random subspace predictor for traffic flow forecasting. IEEE Trans Intell Transp Syst 8:367–373

    Article  Google Scholar 

  • Tan YM, Guo D, Xu B (2015) A geospatial information quantity model for regional landslide risk assessment. Nat Hazards 79:1385–1398. doi:10.1007/s11069-015-1909-1

    Article  Google Scholar 

  • Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28:1088–1099

    Article  Google Scholar 

  • Tin Kam H (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844. doi:10.1109/34.709601

    Article  Google Scholar 

  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (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

    Article  Google Scholar 

  • Tsangaratos P, Ilia I, Hong H, Chen W, Xu C (2017) Applying information theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14:1091–1111. doi:10.1007/s10346-016-0769-4

    Article  Google Scholar 

  • Uhlemann S, Smith A, Chambers J, Dixon N, Dijkstra T, Haslam E, Meldrum P, Merritt A, Gunn D, Mackay J (2016) Assessment of ground-based monitoring techniques applied to landslide investigations. Geomorphology 253:438–451. doi:10.1016/j.geomorph.2015.10.027

    Article  Google Scholar 

  • Varilova Z, Kropacek J, Zvelebil J, St’astny M, Vilimek V (2015) Reactivation of mass movements in Dessie graben, the example of an active landslide area in the Ethiopian Highlands. Landslides 12:985–996. doi:10.1007/s10346-015-0613-2

    Article  Google Scholar 

  • Vilimek V, Smolikova J (2015) Scientific research for landslide risk analysis and international education for mitigation and preparedness. Landslides 12:1227–1231. doi:10.1007/s10346-015-0636-8

    Article  Google Scholar 

  • Vranken L, Vantilt G, Eeckhaut MVD, Vandekerckhove L, Poesen J (2015) Landslide risk assessment in a densely populated hilly area. Landslides 12:787–798

    Article  Google Scholar 

  • Wang GQ, Bao Y, Cuddus Y, Jia XY, Serna J, Jing Q (2015a) A methodology to derive precise landslide displacement time series from continuous GPS observations in tectonically active and cold regions: a case study in Alaska. Nat Hazards 77:1939–1961. doi:10.1007/s11069-015-1684-z

    Article  Google Scholar 

  • Wang LJ, Guo M, Sawada K, Lin J, Zhang JC (2015b) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. CATENA 135:271–282. doi:10.1016/j.catena.2015.08.007

    Article  Google Scholar 

  • Wang Y, Song C, Lin Q, Li J (2016) Occurrence probability assessment of earthquake-triggered landslides with Newmark displacement values and logistic regression: the Wenchuan earthquake, China. Geomorphology 258:108–119. doi:10.1016/j.geomorph.2016.01.004

    Article  Google Scholar 

  • Webster JM, George NPJ, Beaman RJ, Hill J, Puga-Bernabéu Á, Hinestrosa G, Abbey EA, Daniell JJ (2016) Submarine landslides on the Great Barrier Reef shelf edge and upper slope: a mechanism for generating tsunamis on the north-east Australian coast? Mar Geol 371:120–129. doi:10.1016/j.margeo.2015.11.008

    Article  Google Scholar 

  • Wei CH, Qi F (2012) On the estimation and testing of mixed geographically weighted regression models. Econ Model 29:2615–2620. doi:10.1016/j.econmod.2012.08.015

    Article  Google Scholar 

  • Yamao M, Sidle RC, Gomi T, Imaizumi F (2016) Characteristics of landslides in unwelded pyroclastic flow deposits, southern Kyushu, Japan. Nat Hazards Earth Syst Sci 16:617–627. doi:10.5194/nhess-16-617-2016

    Article  Google Scholar 

  • Yang HQ, Lan YF, Lu L, Zhou XP (2015) A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion. Eng Geol 185:20–32. doi:10.1016/j.enggeo.2014.11.016

    Article  Google Scholar 

  • Ye N, Chen Q (2001) An anomaly detection technique based on a Chi square statistic for detecting intrusions into information systems. Qual Reliab Eng Int 17:105–112

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, El-Haddad BA, Dhahry BK (2016) 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 75:63–87. doi:10.1007/s10064-015-0734-9

    Article  Google Scholar 

  • Yusof NM, Pradhan B, Shafri HZM, Jebur MN, 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 8:9789–9800. doi:10.1007/s12517-015-1937-x

    Article  Google Scholar 

  • Zhang X, Jia Y (2007) A linear discriminant analysis framework based on random subspace for face recognition. Pattern Recognit 40:2585–2591

    Article  Google Scholar 

  • Zhang HG, Mei CL (2011) Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches. Int J Geogr Inf Sci 25:1467–1489. doi:10.1080/13658816.2010.528420

    Article  Google Scholar 

  • Zhang CS, Tang Y, Xu XL, Kiely G (2011) Towards spatial geochemical modelling: use of geographically weighted regression for mapping soil organic carbon contents in Ireland. Appl Geochem 26:1239–1248. doi:10.1016/j.apgeochem.2011.04.014

    Article  Google Scholar 

  • Zhang J, Gurung DR, Liu R, Murthy MSR, Su F (2015) Abe Barek landslide and landslide susceptibility assessment in Badakhshan Province, Afghanistan. Landslides 12:597–609. doi:10.1007/s10346-015-0558-5

    Article  Google Scholar 

  • Zhu Y, Liu J, Chen S (2009) Semi-random subspace method for face recognition. Image Vis Comput 27:1358–1370

    Article  Google Scholar 

  • Zhu JX, Zhang LZ, Zhou XY, Liang GL, Wang Q, Cai ZZ (2012) Application of inverse distance weighted method to regional landslide hazards assessment. Bull Soil Water Conserv 32(3):136–140

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Editor-in-chief James W. LaMoreaux and three anonymous reviewers for their meaningful comments on the primary version of the manuscript. This research was supported by the National Natural Science Foundation of China (Project Nos: 41431177, 41601413), the Natural Science Research Programme of Jiangsu (Project No: BK20150975), the Natural Science Research Programme of Jiangsu (Project No: 14KJA170001), and the National Basic Research Programme of China (Project No: 2015CB954102). Support for A-Xing Zhu through the Vilas Associate Award, the Hammel Faculty Fellow Award, The Manasse Chair Professorship from the University of Wisconsin-Madison, and The “One-Thousand Talents” Programme of China is greatly appreciated.

Author information

Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  2. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, China

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  3. Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023, Jiangsu, China

    Haoyuan Hong, Junzhi Liu & A-Xing Zhu

  4. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  5. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Binh Thai Pham

  6. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Wei Chen

  7. Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia

    Biswajeet Pradhan

  8. School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, Building 11, Level 06, 81 Broadway, PO Box 123, Ultimo, NSW, 2007, Australia

    Biswajeet Pradhan

  9. Geographic Information System group, Department of Business and IT, University College of Southeast Norway, N-3800, Bøi Telemark, Norway

    Dieu Tien Bui

Authors
  1. Haoyuan Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junzhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A-Xing Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Himan Shahabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Biswajeet Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A-Xing Zhu or Wei Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, H., Liu, J., Zhu, AX. et al. 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 (2017). https://doi.org/10.1007/s12665-017-6981-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-017-6981-2

Keywords

Search

Navigation