Skip to main content

Advertisement

Log in

Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization

Landslides Aims and scope Submit manuscript

Abstract

The main objective of this study is to produce a landslide susceptibility map for the Lao Cai area (Vietnam) using a new hybrid intelligent method based on least squares support vector machines (LSSVM) and artificial bee colony (ABC) optimization, namely LSSVM-BC. LSSVM and ABC are state-of-the-art soft computing techniques that have been rarely utilized in landslide susceptibility assessment. LSSVM is adopted to develop landslide prediction model whereas ABC was used to optimize the prediction model by identifying an appropriate set of the LSSVM hyper-parameters. To establish the hybrid intelligent method, a GIS database with ten landslide-influencing factors and 340 landslide locations that occurred mainly during the last 20-years was constructed. These historical landslide locations were collected from the existing inventories that sourced from (i) five landslide projects carried out in this study areas before and (ii) interpretations of SPOT satellite images with resolution of 2.5 m. The study area was geographically split into two different parts, with landslides located in the first part was used for building models whereas the other landslides in the second part was used for the model validation. Performance of the LSSVM-BC model was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Result shows that the prediction power of the model is good with the area under the curve (AUC) = 0.900. Experiments have pointed out the prediction power of the LSSVM-BC is better than that obtained from the popular support vector machines. Therefore, the proposed model is a promising tool for spatial prediction of landslides at the study area. The landslide susceptibility map is useful for landuse planning for the Lao Cai area.

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

Access this article

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

Similar content being viewed by others

References

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • 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 

  • Beven K, Kirkby M, Schofield N, Tagg A (1984) Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. J Hydrol 69:119–143

    Article  Google Scholar 

  • Bishop C (2006) Pattern recognition and machine learning. Springer Science + Business Media, Singapore

    Google Scholar 

  • Burrough PA, McDonnell RA (2011) Principles of geographical information systems. Oxford University Press, Oxford

    Google Scholar 

  • Cheng M-Y and Hoang N-D (2014) Slope collapse prediction using Bayesian framework with k-nearest neighbor density estimation: case study in Taiwan. J Comput Civil Eng: 04014116

  • Cheng M-Y, Hoang N-D (2015) Typhoon-induced slope collapse assessment using a novel bee colony optimized support vector classifier. Nat Hazards 78:1961–1978

    Article  Google Scholar 

  • Ching J, Liao H-J, Lee J-Y (2011) Predicting rainfall-induced landslide potential along a mountain road in Taiwan. Geotechnique 61:153–166

    Article  Google Scholar 

  • Chung C-J, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452

    Article  Google Scholar 

  • Clague JJ, Stead D (2012) Landslides: types, mechanisms and modeling. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263

    Google Scholar 

  • Cossart E, Mercier D, Decaulne A, Feuillet T, Jónsson HP, Sæmundsson Þ (2014) Impacts of post‐glacial rebound on landslide spatial distribution at a regional scale in northern Iceland (Skagafjörður). Earth Surf Process Landf 39:336–350

    Article  Google Scholar 

  • 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 Hazards Earth Syst Sci 12:327–340

    Article  Google Scholar 

  • Cukier R, Fortuin C, Shuler KE, Petschek A, Schaibly J (1973) Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I. Theory. J Chem Phys 59:3873–3878

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics, and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K, De Moor B, Vandewalle J and Suykens J (2011) LS-SVMlab toolbox user’s guide. ESAT-SISTA Technical Report: 10–146

  • Dou J, Tien Bui D, P. Yunus A, Jia K, Song X, Revhaug I, Xia H and Zhu Z (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10: e0133262

  • Duan NB, Hai DT, Minh DV, Hien LTT (2011) Studying to determine causes of landslide in the area of the Mong Sen bridge, Lao Cai province. J Sci Earth (Vietnamese) 33:164–174

    Google Scholar 

  • Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172–191

    Article  Google Scholar 

  • Gorsevski PV, Gessler PE, Jankowski P (2003) Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Geograph Syst 5:223–251

    Article  Google Scholar 

  • Gunaratne M (2013) The foundation engineering handbook. CRC Press

  • 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 171:30–41

    Article  Google Scholar 

  • Hoang N-D and Tien Bui D (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. JComput Civil Engi doi:101061/(ASCE)CP1943-54870000557

  • Hong H, Pradhan B, Xu C, Tien Bui D (2015a) 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, Xu C, Revhaug I and Tien Bui D (2015b) Spatial prediction of landslide hazard at the Yihuang area (China): a comparative study on the predictive ability of backpropagation multi-layer perceptron neural networks and radial basic function neural networks. In: Robbi Sluter C, Madureira Cruz CB and Leal de Menezes PM (eds) Cartography - maps connecting the world, Springer International Publishing, pp 175–188

  • Hong H, Pradhan B, Jebur M, Tien Bui D, Xu C, Akgun A (2016a) Spatial prediction of landslide hazard at the luxi area (china) using support vector machines. Environ Earth Sci doi:10.1007/s12665-015-4866-975

  • Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Tien Bui D (2016b) Rainfall-induced landslide susceptibility assessment at the chongren area (china) using frequency ratio, certainty factor, and index of entropy. Geocarto International doi:10.1080/10106049.2015.1130086

  • Hue TT, Duong TV, Toan DV, Nghinh LT, Minh VC, Pho NV, Xuan PT, Hoan LT, Huyen NX, Pha PD, Chinh VV, Thom BV (2004) Investigation and assessment of the types of geological hazard in the territory of Vietnam and recommendation of remedial measures. Phase II: a study of the northern mountainous province of Vietnam. Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, p 361

    Google Scholar 

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  Google Scholar 

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57

    Article  Google Scholar 

  • Kavzoglu T, Sahin E, 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 

  • Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47:411–435

    Article  Google Scholar 

  • Meinhardt M, Fink M, Tünschel 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

    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 

  • Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Ngoi CV, Ha NTT (2008) Assessment of landslide hazards along the national road 4D focusing on the relationship between geologic structures and topology. J Geol (Vietnamese) 305:1–8

    Google Scholar 

  • Pham B, Tien Bui D, Pourghasemi H, Indra P and Dholakia MB (2015) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology: 1–19

  • Pham BT, Bui DT, Dholakia M, Prakash I and 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. Geotechnical and Geological Engineering Doi: 101007/s10706-016-9990-0: 1–18

  • Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016b) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using gis. Natural Hazards doi:10.1007/s11069-016-2304-2

  • Pianosi F, Raso L (2012) Dynamic modeling of predictive uncertainty by regression on absolute errors. Water Resour Res 48:1--11

  • Pianosi F, Wagener T (2015) A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ Model Softw 67:1–11

    Article  Google Scholar 

  • Pianosi F, Sarrazin F, Wagener T (2015) A matlab toolbox for global sensitivity analysis. Environ Model Softw 70:80--85

  • Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw 79:214--232

  • 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 

  • Prosser IP, Rustomji P (2000) Sediment transport capacity relations for overland flow. Prog Phys Geogr 24:179–193

    Article  Google Scholar 

  • QuocPhi N, Phuong N and KimLong N (2012) Statistical and heuristic approaches for spatial prediction of landslide hazards in Laocai, Vietnam. International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Ho Chi Minh city, pp 7

  • Ray RL, Jacobs JM (2007) Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat Hazards 43:211–222

    Article  Google Scholar 

  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M and Tarantola S (2008) Global sensitivity analysis: the primer. John Wiley & Sons

  • Sarrazin F, Pianosi F and Wagener T (2016a) Global sensitivity analysis of environmental models: Convergence and validation. Environ Model Softw 79:135-152

  • Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199

    Article  Google Scholar 

  • Suykens J, Gestel JV, Brabanter JD, Moor BD, Vandewalle J (2002) Least square support vector machines. World Scientific Publishing Co Pte Ltd, Singapore

    Book  Google Scholar 

  • Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Article  Google Scholar 

  • Tien Bui D (2012) Modeling of rainfall-induced landslide hazard for the Hoa Binh province of Vietnam. Norwegian University of Life Sciences. Ph.D Thesis

  • Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen D (2013a) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D (eds) Cartography from pole to pole. Springer, Berlin Heidelberg, pp 303–317

    Google Scholar 

  • Tien Bui D, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I and B Dick O (2012a) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh Province (Vietnam) with kernel functions analysis. International Environmental Modelling and Software Society (iEMSs):

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick O (2013b) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh Province, Vietnam. Nat Hazards 66:707–730

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) 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 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012d) 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

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Revhaug I, Trung Tran C (2014) A comparative assessment between the application of fuzzy unordered rules induction algorithm and J48 decision tree models in spatial prediction of shallow landslides at Lang Son City, Vietnam. In: Mukherjee S, Gupta M, Islam T (eds) Srivastava PK. Springer International Publishing, Remote sensing applications in environmental research, pp 87–111

    Google Scholar 

  • Tien Bui D, Tuan TA, Klempe H, Pradhan B and Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides Doi: 101007/s10346-015-0557-6

  • Tien Bui D, Nguyen Q-P, Hoang N-D, Klempe H (2016a) 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 Doi:101007/s10346-016-0708-4

  • Tien Bui D, Pham TB, Nguyen Q-P, Hoang N-D (2016b) 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. International Journal of Digital Earth Doi:101080/1753894720161169561

  • Trinh PT, Van Liem N, Van Huong N, Vinh HQ, Van Thom B, Thao BT, Tan MT, Hoang N (2012) Late quaternary tectonics and seismotectonics along the Red River fault zone, North Vietnam. Earth Sci Rev 114:224–235

    Article  Google Scholar 

  • Tsangaratos P and Ilia I (2015) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides: 1–16

  • Van Nguyen L, Nguyen NK, Van Hoang H, Tran TQ, Vu NT (2013) Characteristics of groundwater in karstic region in northeastern Vietnam. Environ Earth Sci 70:501–510

    Article  Google Scholar 

  • Were K, Tien Bui D, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52:394–403

    Article  Google Scholar 

  • Wise S (2013) GIS fundamentals. CRC Press

  • 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

    Article  Google Scholar 

  • Yem NT, Thanh NQ, Anh PL, Chi CT, Du CD, Dung NP, Dung PD, Hai NP, Hien TT, Hoang NV, Lien VTH, Phuong CT, Quoc LM, Tuan TA, Thuan PN, Thom BV, Thinh NH (2006) Assessment of landslides and debris flows at some prone mountainous areas Vietnam and recommendation of remedial measures. Phase I: a study of the east side of the Hoang Lien Son mountainous area of Vietnam. Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, p 361

    Google Scholar 

  • Yeon Y-K, Han J-G, Ryu KH (2010) Landslide susceptibility mapping in Injae, Korea, using a decision tree. Eng Geol 116:274–283

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836

    Article  Google Scholar 

  • Zuchiewicz W, Quốc Cu’ò’ng N, Zasadni J, Yêm NT (2013) Late cenozoic tectonics of the red river fault zone, Vietnam, in the light of geomorphic studies. J Geodyn 69:11–30

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Vietnam Academy of Science and Technology (VAST), the grant project code is VAST05.02/14–15. The modeling process was carried out at the Geographic Information System group, University College of Southeast Norway. The first author would like to thank Dr. Francesca Pianosi (Department of Civil Engineering, University of Bristol, England) for sending the SAFE Toolbox for the sensitive analysis.

Author information

Authors and Affiliations

Authors

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

Tien Bui, D., Tuan, T.A., Hoang, ND. et al. Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides 14, 447–458 (2017). https://doi.org/10.1007/s10346-016-0711-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-016-0711-9

Keywords

Navigation