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Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

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  • Ozgur Kisi
  • Alireza Nia
  • Mohsen Gosheh
  • Mohammad Tajabadi
  • Azadeh Ahmadi

Abstract

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. Copyright Springer Science+Business Media B.V. 2012

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  • Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:2:p:457-474
    DOI: 10.1007/s11269-011-9926-7
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    1. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
    2. Mehdi Rezaeian Zadeh & Seifollah Amin & Davar Khalili & Vijay Singh, 2010. "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2673-2688, September.
    3. A. Bhadra & A. Bandyopadhyay & R. Singh & N. Raghuwanshi, 2010. "Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 37-62, January.
    4. Alexandre Evsukoff & Beatriz Lima & Nelson Ebecken, 2011. "Long-Term Runoff Modeling Using Rainfall Forecasts with Application to the Iguaçu River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(3), pages 963-985, February.
    5. Antonia Jones, 2004. "New tools in non-linear modelling and prediction," Computational Management Science, Springer, vol. 1(2), pages 109-149, July.
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    6. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    7. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
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    11. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
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    14. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4661-4679, November.
    15. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    16. Bosy A. El-Haddad & Ahmed M. Youssef & Hamid R. Pourghasemi & Biswajeet Pradhan & Abdel-Hamid El-Shater & Mohamed H. El-Khashab, 2021. "Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 83-114, January.
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    18. Mohammed Seyam & Faridah Othman, 2014. "The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2583-2597, July.
    19. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.
    20. Mohammad R. Hassanvand & Hojat Karami & Sayed-Farhad Mousavi, 2018. "Investigation of neural network and fuzzy inference neural network and their optimization using meta-algorithms in river flood routing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1057-1080, December.
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