Water Resources Management

, Volume 26, Issue 2, pp 457–474 | Cite as

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

  • Ozgur Kisi
  • Alireza Moghaddam Nia
  • Mohsen Ghafari Gosheh
  • Mohammad Reza Jamalizadeh Tajabadi
  • Azadeh Ahmadi


Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.


ANFIS Neural networks Support vector machine Intermittent streamflows Forecast 


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Ozgur Kisi
    • 1
  • Alireza Moghaddam Nia
    • 2
  • Mohsen Ghafari Gosheh
    • 2
  • Mohammad Reza Jamalizadeh Tajabadi
    • 2
  • Azadeh Ahmadi
    • 3
  1. 1.Dept. of Civil Eng., Faculty of Eng.University of ErciyesKayseriTurkey
  2. 2.Dept. of Watershed and Range Management, Faculty of Natural ResourcesUniv. of ZabolZabolIran
  3. 3.Department of Civil EngineeringIsfahan University of TechnologyIsfahanIran

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