Skip to main content
SpringerLink
Log in

Three Different Adaptive Neuro Fuzzy Computing Techniques for Forecasting Long-Period Daily Streamflows

  • Chapter
  • First Online:
Big Data in Engineering Applications

Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

A modeling study was presented here using three different adaptive neuro-fuzzy (ANFIS) approach algorithms comprising grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC) and fuzzy C-Means clustering (ANFIS-FCM) for forecasting long period daily streamflow magnitudes. Long-period data (between 1936 and 2016) from two hydrometric stations in USA were used for training, evaluating and testing the approaches. Five different input combinations were applied based on the autoregressive analysis of the recorded streamflow data. A sensitivity analysis was also carried out to investigate the effect of different model architectures on the obtained outcomes. When using ANFIS-GP, the double-input model gives the best results for different model architectures, while the triple-input models produce the most accurate results using both ANFIS-SC and ANFIS-FCM models, which is due to increasing the model complexity for ANFIS-GP by using more input parameters. Comparing the all three algorithms it was observed that the ANFIS-FCM generally gave the most accurate results among others.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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. Anusree, K., & Varghese, K. O. (2016). Streamflow prediction of Karuvannur River Basin using ANFIS, ANN and MNLR models. Procedia Technology, 24, 101–108.

    Article  Google Scholar 

  2. Aqil, M., Kita, I., Yano, A., et al. (2007). A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behavior of runoff. Journal of Hydrology, 337, 22–34.

    Article  Google Scholar 

  3. Ballini, R., Soares, S., & Andrade, M. G. (1999). Seasonal streamflow forecasting via a neural fuzzy system. In: 14th Triennial World Congress, Beijing, P.R. China (pp. 5249–5254).

    Article  Google Scholar 

  4. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. New York: Plenum.

    Book  MATH  Google Scholar 

  5. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy C-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  6. Chemeda Edossa, D., & Singh Babel, M. (2011). Application of ANN-based streamflow forecasting model for agricultural water management in the Awash River Basin, Ethiopia. Water Resources Management, 25, 1759–1773.

    Article  Google Scholar 

  7. Cobaner, M. (2011). Evapotranspiration estimation by two different neuro-fuzzy inference systems. Journal of Hydrology, 398(3–4), 299–302.

    Google Scholar 

  8. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.

    Article  MathSciNet  MATH  Google Scholar 

  9. El-Shafie, A., Taha, M. R., & Noureldin, A. (2007). A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam. Water Resources Management, 21, 533–556.

    Article  Google Scholar 

  10. Gunduz, O., & Aral, M. M. (2005). River networks and groundwater flow: A simultaneous solution of a coupled system. Journal of Hydrology, 301(1–4), 216–234.

    Article  Google Scholar 

  11. He, Z., wen, X., Liu, H., & Du, J. (2013). A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509, 379–386.

    Article  Google Scholar 

  12. Hiremath, S. M., Patra, S. K., & Mishra, A. K. (2012). ANFIS with subtractive clustering-based extended data rate prediction for cognitive radio. In Proceeding of the 5th International Conference on Computers and Devices for Communication (CODEC). https://doi.org/10.1109/codec.2012.6509239.

  13. Hu, Y. C. (2007). Sugeno fuzzy integral for finding fuzzy if–Then classification rules. Applied Mathematics and Computation, 185, 72–83.

    Article  MathSciNet  MATH  Google Scholar 

  14. Humphrey, G. B., Gibbs, M. S., Dandy, G. C., & Maier, H. R. (2016). A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623–640.

    Article  Google Scholar 

  15. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neurofuzzy and soft computing: A computational approach to learning and machine intelligence. New Jersey: Prentice-Hall.

    Google Scholar 

  16. Kagoda, P. A., Ndiritu, J., Ntuli, C., & Mwaka, B. (2010). Application of radial basis function neural networks to short-term streamflow forecasting. Physics and Chemistry of the Earth, 35(13–14), 571–581.

    Article  Google Scholar 

  17. Kisi, O. (2008). River flow forecasting and estimation using different artificial neural network techniques. Hydrology Research, 39(1), 27–40.

    Article  Google Scholar 

  18. Kisi, O., Hossein zadeh Dalir, A., Cimen, M., & Shiri, J. (2012). Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology, 450–451, 48–58.

    Article  Google Scholar 

  19. Kisi, O., Shiri, J., & Tombul, M. (2013). Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51, 108–117.

    Article  Google Scholar 

  20. Kisi, O., Karimi, S., Shiri, J., Makarynskyy, O., & Yoon, H. (2014). Forecasting sea water levels at Mukho Station, South Korea using soft computing techniques. The International Journal of Ocean and Climate Systems, 5(4), 175–188.

    Article  Google Scholar 

  21. Kisi, O., & Zounemat-Kermani, M. (2016). Suspended sediment modeling using neuro-fuzzy embedded fuzzy c-means clustering technique. Water Resources Management, 30(11), 3979–3994.

    Article  Google Scholar 

  22. Lin, C. T., Lin, C. J., & Lee, C. S. G. (1995). Fuzzy adaptive learning control network with on-line neural learning. Fuzzy Sets Systems, 71, 25–45.

    Article  MathSciNet  Google Scholar 

  23. Maier, H. R., & Dandy, G. C. (1996). Use of artificial neural networks for prediction of water quality parameters. Water Resources Research, 32(4), 1013–1022.

    Article  Google Scholar 

  24. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.

    Article  MATH  Google Scholar 

  25. Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291, 52–66.

    Article  Google Scholar 

  26. Rath, S., Nayak, P. C., & Chatterjee, C. (2013). Hierarchical neuro-fuzzy model for real-time flood forecasting. International Journal of River Basin Management, 11(3), 253–268.

    Article  Google Scholar 

  27. Russel, S. O., & Campbell, P. F. (1996). Reservoir operating rules with fuzzy programming. Journal of Water Resources Planning and Management, 122(3), 165–170.

    Article  Google Scholar 

  28. Sarlak, N. (2008). Annual streamflow modelling with asymmetric distribution function. Hydrological Processes, 22, 3403–3409.

    Article  Google Scholar 

  29. Sharma, S., Srivastava, P., Fang, X., & Kalin, L. (2015). Performance comparison of Adoptive Neuro Fuzzy Inference System (ANFIS) with Loading Simulation Program C++ (LSPC) model for streamflow simulation in El Niño Southern Oscillation (ENSO)-affected watershed. Expert Systems with Applications, 42(4), 2213–2223.

    Article  Google Scholar 

  30. Shiri, J., & Kisi, O. (2010). Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology, 394, 486–493.

    Article  Google Scholar 

  31. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132.

    Article  MATH  Google Scholar 

  32. Talei, A., Chua, L. H., Queck, C., & Jansson, P. E. (2013). Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, 17–32.

    Article  Google Scholar 

  33. Tennant, D. L. (1976). Instream flow regimes for fish, wildlife, recreation and related environmental resources. Fisheries, 1, 6–10.

    Article  Google Scholar 

  34. Vernieuwe, H., Georgieva, O., De Baets, B., Pauwels, V. R. N., Verhoest, N. E. C., & De Troch, F. P. (2005). Comparison of data-driven Takagi-Sugeno models of rainfall-discharge dynamics. Journal of Hydrology, 302(1–4), 173–186.

    Article  Google Scholar 

  35. Wang, W., Van Gelder, P., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324, 383–399.

    Article  Google Scholar 

  36. Wang, W., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374, 294–306.

    Article  Google Scholar 

  37. Yarar, A. (2014). A hybrid wavelet and neuro-fuzzy model for forecasting the monthly streamflow data. Water Resources Management, 28, 553–565.

    Article  Google Scholar 

  38. Yilmaz, A. G., & Muttil, N. (2014). Runoff estimation by machine learning methods and application to the Euphrates Basin in Turkey. Journal of Hydrologic Engineering, 19(5), 1015–1025.

    Article  Google Scholar 

  39. Zounemat Kermani, M., & Teshnelab, M. (2008). Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Applied Soft Computing, 8, 928–936.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  2. Faculty of Agriculture, Water Engineering Department, University of Tabriz, Tabriz, Iran

    Jalal Shiri & Sepideh Karimi

  3. Faculty of Agricultural and Biosystems Engineering and Technology, Muhammad Nawaz Sharif University of Agriculture, Multan, Pakistan

    Rana Muhammad Adnan

Authors
  1. Ozgur Kisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jalal Shiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sepideh Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rana Muhammad Adnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozgur Kisi .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kisi, O., Shiri, J., Karimi, S., Adnan, R.M. (2018). Three Different Adaptive Neuro Fuzzy Computing Techniques for Forecasting Long-Period Daily Streamflows. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8476-8_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation