Skip to main content
SpringerLink
Log in

Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

In Romania, as in the rest of the world, the flood frequency has increased considerably. Prahova river basin is among the most exposed catchments of the country to flood risk. It also represents the area of the present study for which the identification of surfaces with high susceptibility to flood phenomena was attempted by applying 2 hybrid models (adaptive neuro-fuzzy inference system and fuzzy support vector machine hybrid) and 2 bivariate statistical models (certainty factor and statistical index). The computation of Flood Potential Index (FPI) was possible by considering a number of 10 flood conditioning factors together with a number of 158 flood pixels and 158 non-flood pixels. Generally, the high and very high flood potential appears on around 25% of the upper and middle basin of Prahova river. The validation of the results was made through the ROC Curve model. One of the novelties of this research is related to the application of Fuzzy Support Vector Machine ensemble for the first time in a study concerning the evaluation of the susceptibility to a certain natural hazard.

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

Similar content being viewed by others

References

  • Blanco-Vogt A, Schanze J (2014) Assessment of the physical flood susceptibility of buildings on a large scale–conceptual and methodological frameworks. Nat Hazards Earth Syst Sci 14(8):2105–2117

    Article  Google Scholar 

  • Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):948

    Article  Google Scholar 

  • Celikyilmaz A, Turksen IB (2009) Modeling uncertainty with fuzzy logic. Stud Fuzziness Soft Comput 240:149–215

    Article  Google Scholar 

  • Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27

    Article  Google Scholar 

  • Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245

    Article  Google Scholar 

  • Costache R (2019) Flash-flood potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models. Sci Total Environ 659:1115–1134

    Article  Google Scholar 

  • Costache R, Zaharia L (2017) Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment–case study: Bâsca Chiojdului River catchment (Romania). J Earth Syst Sci 126(4):59

    Article  Google Scholar 

  • Fotovatikhah F, Herrera M, Shamshirband S, Chau KW, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: challenges, research directions and future work. Eng Applic Comput Fluid Mech 12(1):411–437

    Article  Google Scholar 

  • Ilia I, Koumantakis I, Rozos D, Koukis G, Tsangaratos P (2015) A geographical information system (GIS) based probabilistic certainty factor approach in assessing landslide susceptibility: the case study of Kimi, Euboea, Greece. Engineering geology for society and territory-volume 2 (pp. 1199–1204). Springer, Cham

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 23(3):665–685

    Article  Google Scholar 

  • Khan A, Baharudin B, Lee LH, Khan K (2010) A review of machine learning algorithms for text-documents classification. J Adv Inform Technol 1(1):4–20

    Google Scholar 

  • Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987

    Article  Google Scholar 

  • Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471

    Article  Google Scholar 

  • Maiti S, Tiwari RK (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environ Earth Sci 71(7):3147–3160

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Appl Geomat 1(1–2):3–15

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR (2017) Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resour Manag 31(5):1473–1487

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Valavi R, Shahabi H, Chapi K, Shirzadi A (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manag 217:1–11

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Vormoor K, Lawrence D, Heistermann M, Bronstert A (2015) Climate change impacts on the seasonality and generation processes of floods–projections and uncertainties for catchments with mixed snowmelt/rainfall regimes. Hydrol Earth Syst Sci 19(2):913–931

    Article  Google Scholar 

  • Wang YM, Elhag TM (2008) An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Syst Appl 34(4):3099–3106

    Article  Google Scholar 

  • Xian GM (2010) An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl 37(10):6737–6741

    Article  Google Scholar 

  • Xu C, Dai FC, Yao X, Chen J, Tu XB, Cao YB, Xiao JZ (2010) GIS based certainty factor analysis of landslide triggering factors in Wenchuan earthquake. Chin J Rock Mech Eng 29(Z1):2972–2981

    Google Scholar 

  • Zaharia L, Costache R, Prăvălie R, Ioana-Toroimac G (2017) Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania). Front Earth Sci 11(2):229–247

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank to the editors and the three anonymous reviewers of the Water Resources Management journal, for their critical and valuable comments that helped to bring the manuscript into its present form. This paper was financially supported by the Research Institute of the University of Bucharest.

Author information

Authors and Affiliations

  1. Research Institute of the University of Bucharest, 36-46 Bd. M. Kogălniceanu, 5th District, 050107, Bucharest, Romania

    Romulus Costache

  2. National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania

    Romulus Costache

Authors
  1. Romulus Costache
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Romulus Costache.

Ethics declarations

Conflict of Interest

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Costache, R. Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management. Water Resour Manage 33, 3239–3256 (2019). https://doi.org/10.1007/s11269-019-02301-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-019-02301-z

Keywords

Search

Navigation