Skip to main content
SpringerLink
Log in

An Integration of Least Squares Support Vector Machines and Firefly Optimization Algorithm for Flood Susceptible Modeling Using GIS

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

Abstract

The main aim of this research is to propose and evaluate a new hybrid intelligent approach (namely LSSVM-FA) based on Least Squared Support Vector Machines (LSSVM) and Firefly algorithm (FA) for flood susceptible modeling with a case study at a typical flood region in Central Vietnam. LSSVM and FA are current state-of-the art machine learning techniques that have rarely been explored for flood study. For this aim, a geospatial database of flood for the study area was constructed that consists of 76 historical flooded locations and 10 influencing factors. Using the database, the flood model was established using LSSVM, and then, the model was optimized where the best model’s parameters were determined using FA. The goodness-of-fit and the prediction capability of the proposed model were evaluated using Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The results showed that the proposed model performs well with the training data (AUC = 0.961) and the validation data (AUC = 0.934). Since the proposed model is better than benchmarks i.e. Neuron-fuzzy, support vector machines, and random forest, it could be concluded that the proposed model is a promising tool that should be used for flood modeling. The result from this research is useful for land-use planning and management at flood-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. Werner, M.: A comparison of flood extent modelling approaches through constraining uncertainties on gauge data. Hydrol. Earth Syst. Sci. 8, 1141–1152 (2004)

    Article  Google Scholar 

  2. Vojinovic, Z., Hammond, M., Golub, D., Hirunsalee, S., Weesakul, S., Meesuk, V., Medina, N., Sanchez, A., Kumara, S., Abbott, M.: Holistic approach to flood risk assessment in areas with cultural heritage: a practical application in Ayutthaya. Thai. Nat. Hazards 81, 589–616 (2016)

    Article  Google Scholar 

  3. Nguyen, P.K.-T., Chua, L.H.-C., Son, L.H.: Flood forecasting in large rivers with data-driven models. Nat. Hazards 71, 767–784 (2014)

    Article  Google Scholar 

  4. Tiwari, M.K., Chatterjee, C.: Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach. J. Hydrol. 394, 458–470 (2010)

    Article  Google Scholar 

  5. Fekete, A.: Validation of a social vulnerability index in context to river-floods in Germany. Nat. Hazards Earth Syst. Sci. 9, 393–403 (2009)

    Article  Google Scholar 

  6. Seckin, N., Cobaner, M., Yurtal, R., Haktanir, T.: Comparison of artificial neural network methods with L-moments for estimating flood flow at ungauged sites: the case of East Mediterranean River Basin. Turk. Water Res. Manage. 27, 2103–2124 (2013)

    Article  Google Scholar 

  7. Sattari, M.T., Pal, M., Apaydin, H., Ozturk, F.: M5 model tree application in daily river flow forecasting in Sohu stream. Turk. Water Res. 40, 233–242 (2013)

    Article  Google Scholar 

  8. Tehrany, M.S., Pradhan, B., Jebur, M.N.: Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J. Hydrol. 512, 332–343 (2014)

    Article  Google Scholar 

  9. Kazakis, N., Kougias, I., Patsialis, T.: Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: application in rhodope-evros region, Greece. Sci. Total Environ. 538, 555–563 (2015)

    Article  Google Scholar 

  10. Liu, K., Li, Z., Yao, C., Chen, J., Zhang, K., Saifullah, M.: Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting. Int. J. Sedim. Res. (2016)

    Google Scholar 

  11. Tien Bui, D., Pradhan, B., Nampak, H., Quang Bui, T., Tran, Q.-A., Nguyen, Q.P.: Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modelling in a high-frequency tropical cyclone area using GIS. J. Hydrol. 540, 317–330 (2016)

    Article  Google Scholar 

  12. Tehrany, M.S., Pradhan, B., Mansor, S., Ahmad, N.: Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125, 91–101 (2015)

    Article  Google Scholar 

  13. Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Bui, D.T., Pham, B.T., Khosravi, K.: A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ. Model Softw. 95, 229–245 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2010)

    Google Scholar 

  16. Wang, S.-Y.S., Promchote, P., Truong, L.H., Buckley, B., Li, R., Gillies, R., Trung, N.T.Q., Guan, B., Minh, T.T.: Changes in the autumn precipitation and tropical cyclone activity over central Vietnam and its east sea. Vietnam J. Earth Sci. 36, 489–496 (2015)

    Google Scholar 

  17. Qi, S., Brown, D.G., Tian, Q., Jiang, L., Zhao, T., Bergen, K.M.: Inundation extent and flood frequency mapping using LANDSAT imagery and digital elevation models. GISci. Remote Sens. 46, 101–127 (2009)

    Article  Google Scholar 

  18. Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., Bai, X.: Flood hazard risk assessment model based on random forest. J. Hydrol. 527, 1130–1141 (2015)

    Article  Google Scholar 

  19. Glenn, E.P., Morino, K., Nagler, P.L., Murray, R.S., Pearlstein, S., Hultine, K.R.: Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J. Arid Environ. 79, 56–65 (2012)

    Article  Google Scholar 

  20. Tucker, C., Sellers, P.: Satellite remote sensing of primary production. Int. J. Remote Sens. 7, 1395–1416 (1986)

    Article  Google Scholar 

  21. Džubáková, K., Molnar, P., Schindler, K., Trizna, M.: Monitoring of riparian vegetation response to flood disturbances using terrestrial photography. Hydrol. Earth Syst. Sci. 19, 195–208 (2015)

    Article  Google Scholar 

  22. Heitmuller, F.T., Hudson, P.F., Asquith, W.H.: Lithologic and hydrologic controls of mixed alluvial–bedrock channels in flood-prone fluvial systems: Bankfull and macrochannels in the Llano River watershed, central Texas, USA. Geomorphology 232, 1–19 (2015)

    Article  Google Scholar 

  23. Suykens, J., Gestel, J.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Square Support Vector Machines. World Scientific Publishing Co. Pte. Ltd, Singapore (2002)

    Book  Google Scholar 

  24. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B.: Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. In: iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, pp. 382–389 (Year)

    Google Scholar 

  25. Tien Bui, D., Anh Tuan, T., Hoang, N.-D., Quoc Thanh, N., Nguyen, B.D., Van Liem, N., Pradhan, B.: Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a novel hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization, Landslides (2016). doi:10.1007/s10346-016-0711-9

  26. Yang, X.-S.: Nature-Inspired Computation in Engineering. Springer (2016)

    Google Scholar 

  27. De Brabanter, K., Karsmakers, P., Ojeda, F., Alzate, C., De Brabanter, J., Pelckmans, K., De Moor, B., Van de Walle, J., Suykens, J.: LS-SVMlab toolbox user’s guide. ESAT-SISTA Technical report, pp. 10–146 (2011)

    Google Scholar 

  28. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B.: Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput. Geosci. 45, 199–211 (2012)

    Article  Google Scholar 

  29. Tien Bui, D., Le, K.-T., Nguyen, V., Le, H., Revhaug, I.: Tropical forest fire susceptibility mapping at the cat ba national park area, Hai Phong city, Vietnam, using GIS-based kernel logistic regression. Remote Sens. 8, 347 (2016)

    Article  Google Scholar 

  30. Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. Wiley, Hoboken (2011)

    Book  Google Scholar 

Download references

Acknowledgement

This research was supported by the Geographic Information System group, University College of Southeast Norway. The data for this research was provided by the Project No. B2014-02-21 (Ministry of Education and Training, Vietnam).

Author information

Authors and Affiliations

  1. Department of Mine Surveying, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam

    Viet-Nghia Nguyen & Nguyen Quoc Long

  2. Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

  3. Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam

    Phuong-Thao Thi Ngo

  4. Faculty of Environmental Sciences, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam

    Quoc-Phi Nguyen

  5. Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Road, Cau Giay, Hanoi, Vietnam

    Van Cam Nguyen

  6. Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, PO.5003IMT, Aas, Norway

    Inge Revhaug

Authors
  1. Viet-Nghia Nguyen
    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. Phuong-Thao Thi Ngo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quoc-Phi Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Van Cam Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nguyen Quoc Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Inge Revhaug
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dieu Tien Bui .

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

Nguyen, VN. et al. (2018). An Integration of Least Squares Support Vector Machines and Firefly Optimization Algorithm for Flood Susceptible Modeling Using GIS. 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_4

Download citation

Publish with us

Policies and ethics

Search

Navigation