Skip to main content
SpringerLink
Log in
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Advances and Applications in Geospatial Technology and Earth Resources (GTER 2017)

Abstract

In the present study, a hybrid approach of Random Subspace Ensemble (RSS) and Reduced Error Pruning Trees (REPT) has been proposed to create a novel hybrid model namely RSS-REPT for landslide susceptibility modeling of the Mu Cang Chai district, Yen Bai province of Vietnam where is affected by a number of landslides every year. For the development of model, a spatial database consisting of 248 historic landslide events and 15 affecting factors (slope, aspect, curvature, plan curvature, profile curvature, elevation, lithology, land use, distance to faults, fault density, distance to roads, road density, distance to rivers, river density, and rainfall), was constructed to generate training and testing datasets. The novel hybrid model was then constructed using training dataset for landslide susceptibility assessment, and its predictive capability was validated using Receiver Operating Characteristic (ROC) curve and Statistical Indexes (SI) analysis. Performance of this novel model has been compared with another popular model namely Support Vector Machines (SVM). Results indicate that its performance (AUC = 0.835) is higher in comparison to the SVM model (AUC = 0.804). Thus the RSS-REPT can be considered as one of the promising methods for better landslide susceptibility assessment of landslide prone areas.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pham, B.T., Bui, D.T., Dholakia, M.B., Prakash, I., Pham, H.V., Mehmood, K., Le, H.Q.: A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics, Natural Hazards and Risk, pp. 1–23 (2016)

    Google Scholar 

  2. Tien Bui, D., Ho, T.-C., Pradhan, B., Pham, B.-T., Nhu, V.-H., Revhaug, I.: GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ. Earth Sci. 75, 1–22 (2016)

    Article  Google Scholar 

  3. Tien Bui, D., Pham, B.T., Nguyen, Q.P., Hoang, N.-D.: 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. Digital Earth 9, 1–21 (2016)

    Article  Google Scholar 

  4. Pham, B.T., Tien Bui, D., Dholakia, M.B., Prakash, I., Pham, H.V.: 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, 1–18 (2016)

    Article  Google Scholar 

  5. Tsangaratos, P., Ilia, I.: Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection. Greece Landslides 13, 305–320 (2016)

    Article  Google Scholar 

  6. Abella, E.A.C., Van Westen, C.J.: Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantánamo. Cuba. Geomorphol. 94, 453–466 (2008)

    Article  Google Scholar 

  7. Shirzadi, A., Bui, D.T., Pham, B.T., Solaimani, K., Chapi, K., Kavian, A., Shahabi, H., Revhaug, I.: Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ. Earth Sci. 76, 60 (2017)

    Article  Google Scholar 

  8. Saha, A.K., Gupta, R.P., Sarkar, I., Arora, M.K., Csaplovics, E.: An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2, 61–69 (2005)

    Article  Google Scholar 

  9. Mathew, J., Jha, V., Rawat, G.: Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. Int. J. Remote Sens. 28, 2257–2275 (2007)

    Article  Google Scholar 

  10. Pham, B.T., Tien Bui, D., Pourghasemi, H.R., Indra, P., Dholakia, M.B.: Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor. Appl. Climatol. 122, 1–19 (2015)

    Article  Google Scholar 

  11. Mathew, J., Jha, V., Rawat, G.: Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6, 17–26 (2009)

    Article  Google Scholar 

  12. Pham, B.T., Pradhan, B., Tien Bui, D., Prakash, I., Dholakia, M.B.: 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 (2016)

    Article  Google Scholar 

  13. Tsangaratos, P., Benardos, A.: Estimating landslide susceptibility through a artificial neural network classifier. Nat. Hazards 74, 1489–1516 (2014)

    Article  Google Scholar 

  14. Pham, B.T., Bui, D.T., Prakash, I., Dholakia, M.: 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. Geomatics 10, 71–79 (2016)

    Google Scholar 

  15. Pham, B.T., Tien Bui, D., Prakash, I., Dholakia, M.B.: Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Nat. Hazards 83, 1–31 (2016)

    Article  Google Scholar 

  16. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)

    Article  Google Scholar 

  17. Xia, J., Dalla Mura, M., Chanussot, J., Du, P., He, X.: Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 53, 4768–4786 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27, 221–234 (1987)

    Article  Google Scholar 

  20. Nefeslioglu, H., Sezer, E., Gokceoglu, C., Bozkir, A., Duman, T.: Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathe. Probl. Eng. 2010 (2010)

    Google Scholar 

  21. Galathiya, A., Ganatra, A., Bhensdadia, C.: Improved decision tree induction algorithm with feature selection, cross validation, model complexity and reduced error pruning. Int. J. Comput. Sci. Inf. Technol. 3, 3427–3431 (2012)

    Google Scholar 

  22. Pham, B.T., Tien Bui, D., Indra, P., Dholakia, M.: 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 (2015)

    Google Scholar 

  23. Gorsevski, P.V., Gessler, P.E., Foltz, R.B., Elliot, W.J.: Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans. GIS 10, 395–415 (2006)

    Article  Google Scholar 

  24. Bennett, N.D., Croke, B.F., Guariso, G., Guillaume, J.H., Hamilton, S.H., Jakeman, A.J., Marsili-Libelli, S., Newham, L.T., Norton, J.P., Perrin, C.: Characterising performance of environmental models. Environ. Model Softw. 40, 1–20 (2013)

    Article  Google Scholar 

  25. Marjanović, M., Kovačević, M., Bajat, B., Voženílek, V.: Landslide susceptibility assessment using SVM machine learning algorithm. Eng. Geol. 123, 225–234 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Wu, Y., Li, W.: GIS-based landslide susceptibility analysis using support vector machine model at a regional scale. Electron. J. Geotech. Eng. 21, 6938–6945 (2016)

    Google Scholar 

  28. NCEP: Global weather data for SWAT (2014). http://globalweather.tamu.edu/home

  29. Pham, B.T., Bui, D.T., Prakash, I.: Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and j48 decision trees methods: a comparative study. Geotech. Geol. Eng., 1–15 (2017)

    Google Scholar 

  30. Brodley, C.E., Utgoff, P.E.: Multivariate decision trees. Mach. Learn. 19, 45–77 (1995)

    Google Scholar 

  31. Tama, B.A., Rhee, K.-H.: Tree-based classifier ensembles for early detection method of diabetes: an exploratory study. Artif. Intell. Rev., 1–16 (2017)

    Google Scholar 

  32. Pham, B.T., Tien Bui, D., Pham, H.V., Le, H.Q., Prakash, I., Dholakia, M.B.: Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: a case study at Mu Cang Chai District, Yen Bai Province (Viet Nam). J. Indian Soc. Remote Sens., 1–11 (2016)

    Google Scholar 

  33. Pham, B.T., Tien Bui, D., Prakash, I., Nguyen, L.H., Dholakia, M.B.: 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 (2017)

    Article  Google Scholar 

  34. Frye, C.: About the geometrical interval classification method (2007). http://blogs.esri.com/esri/arcgis

  35. Pham, B.T., Tien Bui, D., Prakash, I., Dholakia, M.B.: Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149(Part 1), 52–63 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

Authors are thankful to the Vietnam Institute of Geosciences and Mineral Resources for sharing the data. Authors are also thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics, Gujarat, India for providing facilities to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Viet Nam

    Binh Thai Pham

  2. Department of Science and Technology, Bhaskarcharya 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. Indra Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Binh Thai Pham .

Editor information

Editors and Affiliations

  1. GIS group, Department of Business and IT, University College of Southeast Norway , Bo i Telemark, Norway

    Dieu Tien Bui

  2. Department of Underground and Mining Construction, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Anh Ngoc Do

  3. Department of Exploration Geology, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Hoang-Bac Bui

  4. Faculty of Civil Engineering, Duy Tan University , Da Nang, Vietnam

    Nhat-Duc Hoang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Pham, B.T., Prakash, I. (2018). 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) Advances and Applications in Geospatial Technology and Earth Resources. GTER 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-68240-2_16

Download citation

Publish with us

Policies and ethics

Search

Navigation