Skip to main content
SpringerLink
Log in

Flood flow forecasting using ANN, ANFIS and regression models

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

Abstract

Flood prediction is an important for the design, planning and management of water resources systems. This study presents the use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), multiple linear regression (MLR) and multiple nonlinear regression (MNLR) for forecasting maximum daily flow at the outlet of the Khosrow Shirin watershed, located in the Fars Province of Iran. Precipitation data from four meteorological stations were used to develop a multilayer perceptron topology model. Input vectors for simulations included the original precipitation data, an area-weighted average precipitation and antecedent flows with one- and two-day time lags. Performances of the models were evaluated with the RMSE and the R 2. The results showed that the area-weighted precipitation as an input to ANNs and MNLR and the spatially distributed precipitation input to ANFIS and MLR lead to more accurate predictions (e.g., in ANNs up to 2.0 m3 s−1 reduction in RMSE). Overall, the MNLR was shown to be superior (R 2 = 0.81 and RMSE = 0.145 m3 s−1) to ANNs, ANFIS and MLR for prediction of maximum daily flow. Furthermore, models including antecedent flow with one- and two-day time lags significantly improve flow prediction. We conclude that nonlinear regression can be applied as a simple method for predicting the maximum daily flow.

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
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Aqil M, Kita I, Yano A, Nishiyama S (2007) A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J Hydrol 337:22–34

    Article  Google Scholar 

  2. Azamathulla HMD, Ab Ghani A (2011) ANFIS-based approach for predicting the scour depth at culvert outlets. ASCE J Pipeline Syst Eng Pract 2(1):35–40

    Article  Google Scholar 

  3. Bhatt VK, Tiwari AK (2008) Estimation of peak streamflows through channel geometry. Hydrol Sci J 53(2):401–408

    Article  Google Scholar 

  4. Bilgili M (2010) Prediction of soil temperature using regression and artificial neural network models. Meteorol Atmos Phys 110:59–70

    Article  Google Scholar 

  5. Chau K, Wu C, Li Y (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrol Eng 10(6):485–491

    Article  Google Scholar 

  6. Dastorani MT, Moghadamnia AR, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monit Assess 166:421–434

    Article  Google Scholar 

  7. Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol Sci J 43(1):47–66

    Article  Google Scholar 

  8. Engeland K, Hisdal H (2009) A comparison of low flow estimates in ungauged catchments using regional regression and the HBV-model. Water Resour Manag 23(12):2567–2586

    Article  Google Scholar 

  9. Eslamian S, Ghasemizadeh M, Biabanaki M, Talebizadeh M (2010) A principal component regression method for estimating low flow index. Water Resour Manag 24(11):2553–2566

    Article  Google Scholar 

  10. Govindaraju RS, Rao AR (2000) Artificial neural networks in hydrology. Kluwer Academic Publisher, Netherlands

    Book  Google Scholar 

  11. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  12. Jang JSR, Gulley N (1995) The Fuzzy Logic Toolbox for use with MATLAB. The Mathworks Inc., Natick

    Google Scholar 

  13. Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406

    Article  Google Scholar 

  14. Kisi O (2004) River flow modeling using artificial neural network. ASCE J Hydrol Eng 9(1):60–63

    Article  Google Scholar 

  15. Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. ASCE J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  16. Kisi O (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1):27–40

    Article  Google Scholar 

  17. Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 26(2):457–474

    Article  Google Scholar 

  18. Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modeling using artificial neural networks comparison of network types. Hydrol Process 19:1277–1291

    Article  Google Scholar 

  19. Lin CT, Lee CSG (1995) Neural fuzzy system. Prentice Hall, Englewood Cliffs

    Google Scholar 

  20. Maier HR, Dandy GC (1998) The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ Model Softw 13(2):193–209

    Article  Google Scholar 

  21. Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–123

    Article  Google Scholar 

  22. Marofi S, Tabari H, Abyaneh HZ (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manag 25:1417–1435

    Article  Google Scholar 

  23. Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system technique. Adv Water Resour 32(1):88–97

    Article  Google Scholar 

  24. Moradkhani H, Hsu K, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295(1–4):246–262

    Article  Google Scholar 

  25. More JJ (1977) The Levenberg–Marquardt algorithm: implementation and theory, numerical analysis. In: Watson GA (ed) Lecture notes in mathematics, vol 630. Springer, New York, pp 105–116

    Google Scholar 

  26. Mukerji A, Chatterjee C, Raghuwanshi N (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA Models. J Hydrol Eng 14(6):647–652

    Article  Google Scholar 

  27. Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process 22(26):5097–5106

    Article  Google Scholar 

  28. Ozbayoglu G, Ozbayoglu ME (2006) A new approach for the prediction of ash fusion temperatures: a case study using Turkish lignites. Fuel 85:545–552

    Article  Google Scholar 

  29. Rezaeian Zadeh M, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688

    Article  Google Scholar 

  30. Rezaeianzadeh M, Stein A, Tabari H, Abghari H, Jalalkamali N, Hosseinipour EZ, Singh VP (2013) Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. Int J Environ Sci Technol. doi:10.1007/s13762-013-0209-0

  31. Sabziparvar A A, Tabari H (2010) Comparison of artificial neural network models and non-linear regression methods for estimation of potato crop evapotranspiration in a semi-arid region of Iran. In: The international conference on intelligent network and computing, Nov 26–28, Kuala Lumpur, Malaysia

  32. Sabziparvar AA, Parandeh A, Lashkari H, Yazdanpanah H (2010) Mid-level synoptic analysis of flood-generating systems in South-west of Iran (case study: Dalaki watershed river basin). Nat Hazards Earth Syst Sci 10:2269–2279. http://www.nat-hazards-earth-syst-sci.net/10/2269/2010/nhess-10-2269-2010.pdf

  33. Shamseldin AY (1997) Application of a neural network technique to rainfall-runoff modeling. J Hydrol 199:272–294

    Article  Google Scholar 

  34. Shamseldin AY (2010) Artificial neural network model for river flow forecasting in a developing country. J Hydroinform 12(1):22–35

    Google Scholar 

  35. Stromberg D (2007) Natural disasters, economic development, and humanitarian aid. J Econ Perspect 21(3):199–222

    Article  MathSciNet  Google Scholar 

  36. Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330

    Article  Google Scholar 

  37. Tabari H, Marofi S, Abyaneh HZ, Sharifi MR (2010) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput Appl 19:625–635

    Article  Google Scholar 

  38. Tabari H, Marofi S, Sabziparvar AA (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406

    Article  Google Scholar 

  39. Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110:135–142

    Article  Google Scholar 

  40. Tokar AS, Johnson A (1999) Rainfall-runoff modeling using artificial neural networks. ASCE J Hydrol Eng 4(3):232–239

    Article  Google Scholar 

  41. Wharton G, Tomlinson JJ (1999) Flood discharge estimation from river channel dimensions: results of applications in Java, Burundi, Ghana and Tanzania. Hydrol Sci J 44(1):1–17

    Article  Google Scholar 

  42. Wheater HS, Jakeman AJ, Beven KJ (1993) Progress and directions in rainfall-runoff modeling. In: Jakeman AJ, Beck MB, McAleer MJ (eds) Modelling change in environmental systems. Wiley, Chichester

    Google Scholar 

  43. Yonaba H, Anctil F, Fortin V (2010) Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. ASCE J Hydrol Eng 15(4):275–283

    Article  Google Scholar 

  44. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

The data used to carry out this research were provided by the Islamic Republic of Iran Meteorological Office (IRIMO) and Surface Water Office of Fars Regional Water Affair. The first author would like to especially thank Mr. Behzad Shifteh Somee, Prof. Alfred Stein, Dr. Amir AghaKouchak and Prof. Dawei Han for their gracious helps. The first author was partially funded by the Center for Forest Sustainability through the Peaks of Excellence Program at Auburn University.

Author information

Authors and Affiliations

  1. School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849, USA

    M. Rezaeianzadeh & L. Kalin

  2. Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    H. Tabari

  3. Department of Water Engineering, University of Ferdowsi, Mashhad, Iran

    A. Arabi Yazdi

  4. Turgut Ozal University, Ankara, 06010, Turkey

    S. Isik

Authors
  1. M. Rezaeianzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Tabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Arabi Yazdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Isik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Kalin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Rezaeianzadeh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rezaeianzadeh, M., Tabari, H., Arabi Yazdi, A. et al. Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput & Applic 25, 25–37 (2014). https://doi.org/10.1007/s00521-013-1443-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1443-6

Keywords

Search

Navigation