Skip to main content

Advertisement

SpringerLink
Log in

Bagging based Support Vector Machines for spatial prediction of landslides

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

Abstract

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.

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

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44:47–70

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Google Scholar 

  • Büchlmann P, Yu B (2002) Analyzing bagging. Ann Stat 30:927–961

    Article  Google Scholar 

  • Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167

    Article  Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

  • 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

    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. https://doi.org/10.1007/s10064-017-1010-y

  • 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. https://doi.org/10.1007/s10064-017-1004-9

  • Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017e) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N (2017g) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomat Nat Hazards Risk 8:1–23

    Article  Google Scholar 

  • Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64

    Article  Google Scholar 

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324

    Article  Google Scholar 

  • Das I, Sahoo S, van Westen C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114:627–637

    Article  Google Scholar 

  • Dudoit S, Fridlyand J (2003) Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19:1090–1099

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2014) An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. Int J Geogr Inf Sci 28:610–638

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T, Nazmfar H (2014a) GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth 7:688–708

    Article  Google Scholar 

  • Feizizadeh B, Jankowski P, Blaschke T (2014b) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput Geosci 64:81–95

    Article  Google Scholar 

  • Feizizadeh B, Roodposhti MS, Jankowski P, Blaschke T (2014c) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221

    Article  Google Scholar 

  • Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10:122

    Article  Google Scholar 

  • Hoang N-D, Bui DT (2016) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-016-0924-0

    Google Scholar 

  • Hong H, Liu J, Tien Bui D, Pradhan B, Acharya TD, Pham BT, Zhu A-X, Chen W, Bin Ahmad B (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Article  Google Scholar 

  • Hung PV, Son PQ, Dung NV (2016) The study evaluated arming of risk of lanslide in Hoa Binh and Son La reservoir hydropower area on the basis of analyzing high-resolution remote sensing and geographic information systems. Vietnam J Earth Sci 37:193–203

    Google Scholar 

  • Inoue A, Kilian L (2008) How useful is bagging in forecasting economic time series? A case study of US consumer price inflation. J Am Stat Assoc 103:511–522

    Article  Google Scholar 

  • Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101:631–642

    Article  Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Article  Google Scholar 

  • Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Tien Bui D (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at haraz watershed Northern Iran. Sci Total Environ.https://doi.org/10.1016/jscitotenv201801266

    Google Scholar 

  • Kitagawa H, Manabe K, Esguerra E (1991) Bagging of fruit on the tree to control disease. Frontier Trop Fruit Res 321:871–875

    Google Scholar 

  • Kohavi R (1996) Scaling up the accuracy of Naive–Bayes classifiers: a decision-tree hybrid. In: KDD, pp 202–207

  • Kotsiantis SB, Zaharakis ID, Pintelas PE (2006) Machine learning: a review of classification and combining techniques. Artif Intell Rev 26:159–190

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46:33–57

    Article  Google Scholar 

  • Murphy KP (2006) Naive Bayes classifiers. University of British Columbia, Vancouver

    Google Scholar 

  • NCEP (2014) Global weather data for SWAT. https://globalweather.tamu.edu/

  • North MA (2009) A method for implementing a statistically significant number of data classes in the Jenks algorithm. In: 2009 FSKD’09 sixth international conference on fuzzy systems and knowledge discovery. IEEE, pp 35–38

  • Pham BT, Prakash I (2017a) Evaluation and comparison of LogitBoost ensemble, Fisher’s linear discriminant analysis, logistic regression, and support vector machines methods for landslide susceptibility mapping. Geocarto Int. https://doi.org/10.1080/10106049.2017.1404141

  • Pham BT, Prakash I (2017b) A novel hybrid intelligent approach of random subspace ensemble and reduced error pruning trees for landslide susceptibility modeling: a case study at Mu Cang Chai District, Yen Bai Province, Viet Nam. In: Tien Bui D, Ngoc Do A, Bui HB, Hoang ND (eds) International conference on geo-spatial technologies and earth resources. Springer, pp 255–269

  • Pham BT, Prakash I (2017c) A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1202-5

  • Pham BT, Bui DT, Prakash I (2017a) Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech Geol Eng 35:1–15

    Article  Google Scholar 

  • Pham BT, Khosravi K, Prakash I (2017b) Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India. Environ Process 4:1–20

    Article  Google Scholar 

  • Pham BT, Prakash I, Bui DT (2017c) Spatial prediction of landslides using hybrid machine learning approach based on Random Subspace and Classification and Regression Trees. Geomorphology 303:1–15

    Google Scholar 

  • Pham BT, Nguyen V-T, Ngo V-L, Trinh PT, Ngo HTT, Bui DT (2017d) A novel hybrid model of rotation forest based functional trees for landslide susceptibility mapping: a case study at Kon Tum Province, Vietnam. In: Tien Bui D, Ngoc Do A, Bui HB, Hoang ND (eds) International conference on geo-spatial technologies and earth resources. Springer, pp 186–201

  • Pham BT, Tien Bui D, Prakash I, Nguyen LH, Dholakia MB (2017e) A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ Earth Sci 76:371. https://doi.org/10.1007/s12665-017-6689-3

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996

    Article  Google Scholar 

  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013a) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122:349–369

    Article  Google Scholar 

  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013b) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 2:349–369

    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. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199

    Article  Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106

    Google Scholar 

  • Sdao F, Lioi D, Pascale S, Caniani D, Mancini I (2013) Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat Hazards Earth Syst Sci 13:395

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219

    Article  Google Scholar 

  • Tien Bui D, Pham BT, Nguyen QP, Hoang N-D (2016) Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam. Int J Digit Earth 9:1–21. https://doi.org/10.1080/17538947.2016.1169561

    Article  Google Scholar 

  • Tien Bui D, Nguyen QP, Hoang N-D, 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

    Article  Google Scholar 

  • Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. CATENA 145:164–179

    Article  Google Scholar 

  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. CATENA 118:124–135

    Article  Google Scholar 

  • Van Liem N, Dat NP, Dieu BT, Van Phai V, Trinh PT, Vinh HQ, Van Phong T (2016) Assessment of geomorphic processes and active tectonics in Con Voi mountain range area (Northern Vietnam) using the hypsometric curve analysis method. Vietnam J Earth Sci 38:202–216

    Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO Press, Paris, p 63

    Google Scholar 

  • Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V (2001) Feature selection for SVMs. In: Leen TK, Diet-terich TG, Tresp V (eds) Advances in neural information processing systems 13. Massachusetts Institute of Technology, pp 668–674

  • Xu H, Caramanis C, Mannor S (2009) Robustness and regularization of support vector machines. J Mach Learn Res 10:1485–1510

    Google Scholar 

  • Xu C, Xu X, Yao Q, Wang Y (2013) GIS-based bivariate statistical modelling for earthquake-triggered landslides susceptibility mapping related to the 2008 Wenchuan earthquake, China. Q J Eng Geol Hydrogeol 46:221–236

    Article  Google Scholar 

  • Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA 72:1–12

    Article  Google Scholar 

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on Support Vector Machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582. https://doi.org/10.1016/j.geomorph.2008.02.011

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Vietnam Institute of Geosciences and Mineral Resources for sharing the data and the Director, BISAG, DST, GOG, India, for the encouragement and facilities for conducting this research.

Author information

Authors and Affiliations

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

    Binh Thai Pham

  2. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  3. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  4. Department of Science & Technology, Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

    Indra Prakash

Authors
  1. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Indra Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dieu Tien Bui.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, B.T., Tien Bui, D. & Prakash, I. Bagging based Support Vector Machines for spatial prediction of landslides. Environ Earth Sci 77, 146 (2018). https://doi.org/10.1007/s12665-018-7268-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-018-7268-y

Keywords

Search

Navigation