Skip to main content
SpringerLink
Log in

Comparison of fuzzy inference algorithms for stream flow prediction

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Fuzzy logic is, inter alia, a simple and flexible approach of modelling that can be used in river basins where adequate hydrological data are unavailable. In order to improve the real-time forecasting of floods, this paper proposes a Takagi–Sugeno fuzzy inference system termed as flood model Sugeno. A total of 12 input parameters were used to develop two fuzzy flood models—Mamdani and Sugeno. Whereas Sugeno FIS performed exceptionally well in predicting the river discharge, the Mamdani FIS failed to deliver the accurate results. The river Jhelum flowing through the Kashmir Valley in the northern Himalayas, India, was hit in September 2014 by a major flood and has been chosen as the case study to apply the fuzzy flood models. With a total of 24 rules in the rule base and five levels of linguistic variables, the flood model Sugeno predicted the river discharge with Nash–Sutcliffe model efficiency of 0.887, coefficient of correlation (R2) of 90.74%, mean square error of 0.00122, root mean square error of 0.0349, mean absolute error of 0.0139 and combined accuracy of 0.0466. The efficiencies of the developed model show acceptable levels according to the tested performance indicators implying the potential of establishing a flood forecasting system using the developed model.

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

Similar content being viewed by others

References

  1. Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature review. Water (Switzerland) 10:1–40. https://doi.org/10.3390/w10111536

    Article  Google Scholar 

  2. Jongman B, Wagemaker J, Romero BR, De Perez EC (2015) Early flood detection for rapid humanitarian response: harnessing near real-time satellite and Twitter signals. ISPRS Int J Geoinf 4:2246–2266. https://doi.org/10.3390/ijgi4042246

    Article  Google Scholar 

  3. Baharom AS, Idris Z, Sarah S et al (2014) Prediction of flood detection system: fuzzy logic approach. Int J Enhanc Res Sci Technol Eng 3:335–339

    Google Scholar 

  4. Rafieeinasab A, Norouzi A, Kim S et al (2015) Toward high-resolution flash flood prediction in large urban areas—analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling. J Hydrol. https://doi.org/10.1016/j.jhydrol.2015.08.045

    Article  Google Scholar 

  5. Yu W, Nakakita E, Jung K (2016) Flood forecast and early warning with high-resolution ensemble rainfall from numerical weather prediction model. Procedia Eng 154:498–503. https://doi.org/10.1016/j.proeng.2016.07.544

    Article  Google Scholar 

  6. Suchkova KV, Motovilov YG (2019) Sensitivity assessment of a runoff formation model in the Mozhaisk reservoir river basin. Water Resour 46:S40–S50. https://doi.org/10.1134/S009780781908013X

    Article  Google Scholar 

  7. Jayawardena AW, Perera EDP, Zhu B et al (2014) A comparative study of fuzzy logic systems approach for river discharge prediction. J Hydrol 514:85–101. https://doi.org/10.1016/j.jhydrol.2014.03.064

    Article  Google Scholar 

  8. Sun W, Trevor B (2015) A comparison of fuzzy logic models for breakup forecasting of the Athabasca river. CGU HS Committee on River Ice Processes and the environment. Presented at the 18th workshop on the hydraulics of Ice Covered Rivers, Quebec City, QC, Canada, 18–21 August 2015. http://cripe.ca/docs/proceedings/18/25_Sun_Trevor_2015.pdf

  9. Nandalal HK, Ratnayake UR (2011) Flood risk analysis using fuzzy models. J Flood Risk Manag 4:128–139. https://doi.org/10.1111/j.1753-318X.2011.01097.x

    Article  Google Scholar 

  10. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

  11. Kim B, Choi SY, Han KY (2019) Integrated real-time flood forecasting and inundation analysis in small–medium streams. Water (Switzerland) 11:1–19. https://doi.org/10.3390/w11050919

    Article  Google Scholar 

  12. Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43. https://doi.org/10.1016/j.jhydrol.2007.10.050

    Article  Google Scholar 

  13. Wang Y, Hong H, Chen W et al (2019) Flood susceptibility mapping in Dingnan county (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J Environ Manag 247:712–729. https://doi.org/10.1016/j.jenvman.2019.06.102

    Article  Google Scholar 

  14. Dubois D, Esteva F, Godo L, Prade H (2007) Fuzzy-set based logics—a history-oriented presentation of their main developments. Handb Hist Log 8:1–128. https://doi.org/10.1016/S1874-5857(07)80009-4

    Article  MathSciNet  Google Scholar 

  15. Bhat MS, Alam A, Ahmad B et al (2019) Flood frequency analysis of river Jhelum in Kashmir basin. Quat Int 507:288–294. https://doi.org/10.1016/j.quaint.2018.09.039

    Article  Google Scholar 

  16. Akhter M, Ahmad AM (2017) Environment pollution and climate change-climate modeling of Jhelum River basin—a comparative study. Environ Pollut Clim Change 1:1–14

    Google Scholar 

  17. Beniston M (2003) Climatic change in mountain regions: a review of possible impacts. Clim Change 59:5–31

    Article  Google Scholar 

  18. Jain M, Parvaze S (2017) Hydrodynamic modelling of extreme flood events in the Kashmir valley in India. Geophys Res Abstr 19:12021

    Google Scholar 

  19. Bhutiyani MR, Kale VS, Pawar NJ (2010) Climate change and the precipitation variations in the northwestern Himalaya: 1866–2006. Int J Climatol 548:535–548. https://doi.org/10.1002/joc.1920

    Article  Google Scholar 

  20. Arora M, Kumar N, Kumar R (2016) An analysis of causes and circumstances of extreme floods in Jhelum basin. J Indian Water Resour Soc 36:15–21

    Google Scholar 

  21. Rao GS, Farooq M, Sree M (2016) Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India. Geomat Nat Hazards Risk. https://doi.org/10.1080/19475705.2016.1218943

    Article  Google Scholar 

  22. Saleh SF, Rather FF, Jabbar MJ (2017) Floods and mitigation techniques with reference to Kashmir. Int J Eng Sci Comput 7:6359–6363

    Google Scholar 

  23. Güçlü YS, Zekai S (2016) Hydrograph estimation with fuzzy chain model. J Hydrol 538:587–597. https://doi.org/10.1016/j.jhydrol.2016.04.057

    Article  Google Scholar 

  24. Lohani AK, Goel NK, Bhatia KKS (2014) Improving real time flood forecasting using fuzzy inference system. J Hydrol 509:25–41. https://doi.org/10.1016/j.jhydrol.2013.11.021

    Article  Google Scholar 

  25. Altunkaynak A, Özger M, Çakmakci M (2005) Water consumption prediction of Istanbul City by using fuzzy logic approach. Water Resour Manag 19:641–654. https://doi.org/10.1007/s11269-005-7371-1

    Article  Google Scholar 

  26. Alvisi S, Mascellani G, Franchini M (2006) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol Earth Syst Sci 10:1–17

    Article  Google Scholar 

  27. Jacquin AP, Shamseldin AY (2006) Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems. J Hydrol 329:154–173. https://doi.org/10.1016/j.jhydrol.2006.02.009

    Article  Google Scholar 

  28. See L, Openshaw S (2000) A hybrid multi-model approach to river level forecasting. Hydrol Sci J 45:523–536. https://doi.org/10.1080/02626660009492354

    Article  Google Scholar 

  29. Mahabir C, Hicks FE, Fayek AR (2003) Application of fuzzy logic to forecast seasonal runoff. Hydrol Process 17:3749–3762. https://doi.org/10.1002/hyp.1359

    Article  Google Scholar 

  30. Kişi Ö (2004) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49:1025–1040. https://doi.org/10.1623/hysj.49.6.1025.55720

    Article  Google Scholar 

  31. Luchetta A, Manetti S (2003) A real time hydrological forecasting system using a fuzzy clustering approach. Comput Geosci 29:1111–1117. https://doi.org/10.1016/S0098-3004(03)00137-7

    Article  Google Scholar 

  32. Foley M, Mcgrory J (2011) The application of fuzzy logic in determining linguistic rules and associative membership functions for the control of a manufacturing process. Dublin Institute of Technology, Dublin

    Google Scholar 

  33. Yildiz ZC (2010) A short fuzzy logic tutorial. Bilkent University, Turkey, pp 1–6. http://cs.bilkent.edu.tr/~zeynep/files/short_fuzzy_logic_tutorial.pdf

  34. Romshoo SA, Altaf S, Rashid I et al (2018) Climatic, geomorphic and anthropogenic drivers of the 2014 extreme flooding in the Jhelum basin of Kashmir, India. Geomat Nat Hazards Risk. https://doi.org/10.1080/19475705.2017.1417332

    Article  Google Scholar 

  35. Eptisa (2018) Jhelum and Tawi flood recovery Project-Part A. The World Bank Group, vol 1, pp 1–145. http://jtfrp.in/wp-content/uploads/2019/03/Task1_Report_Final.pdf

  36. Rashetnias S (2016) Flood vulnerability assessment by applying a fuzzy logic method: a case study from Melbourne. Victoria University, Melbourne

    Google Scholar 

  37. Mekanik F, Imteaz MA, Talei A (2016) Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Clim Dyn 46:3097–3111. https://doi.org/10.1007/s00382-015-2755-2

    Article  Google Scholar 

  38. Nguyen P, Thorstensen A, Sorooshian S et al (2015) A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling. J Hydrol. https://doi.org/10.1016/j.jhydrol.2015.10.047

    Article  Google Scholar 

  39. Chapi K, Singh VP, Shirzadi A et al (2017) Environmental modelling & software: a novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245. https://doi.org/10.1016/j.envsoft.2017.06.012

    Article  Google Scholar 

  40. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13. https://doi.org/10.1016/S0020-7373(75)80002-2

    Article  MATH  Google Scholar 

  41. Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci (NY) 36:59–83. https://doi.org/10.1016/0020-0255(85)90026-X

    Article  MathSciNet  MATH  Google Scholar 

  42. Perera Edangodage Duminda, Pradeep Lahat L (2014) Fuzzy logic based flood forecasting model for the Kelantan river basin. J Hydroenviron Res. https://doi.org/10.1016/j.jher.2014.12.001

    Article  Google Scholar 

  43. Tayfur G, Moramarco T, Singh VP (2007) Predicting and forecasting flow discharge at sites receiving significant lateral inflow. Hydrol Process. https://doi.org/10.1002/hyp.6320

    Article  Google Scholar 

  44. Tareghian R, Kashefipour S (2007) Applications of fuzzy systems and artificial neural networks for flood forecasting. J Appl Sci 7:3451–3459

    Article  Google Scholar 

  45. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2005) Short-term flood forecasting with a neuro-fuzzy model. Water Resour Res 41:1–16. https://doi.org/10.1029/2004WR003562

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support rendered by the Ministry of Human Resources and Development, India, for carrying out the research.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar, Jammu & Kashmir, 190006, India

    Ruhhee Tabbussum & Abdul Qayoom Dar

Authors
  1. Ruhhee Tabbussum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdul Qayoom Dar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruhhee Tabbussum.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Tabbussum, R., Dar, A.Q. Comparison of fuzzy inference algorithms for stream flow prediction. Neural Comput & Applic 33, 1643–1653 (2021). https://doi.org/10.1007/s00521-020-05098-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05098-w

Keywords

Search

Navigation