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Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

Author

Listed:
  • Sajjad Abdollahi

    (Shahid Chamran University)

  • Jalil Raeisi

    (Shahrekord University)

  • Mohammadreza Khalilianpour

    (Shahrekord University)

  • Farshad Ahmadi

    (Shahid Chamran University)

  • Ozgur Kisi

    (Ilia State University)

Abstract

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R2) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:15:d:10.1007_s11269-017-1782-7
    DOI: 10.1007/s11269-017-1782-7
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    Cited by:

    1. Sajjad Abdollahi & Ali Mohammad Akhoond-Ali & Rasoul Mirabbasi & Jan Franklin Adamowski, 2019. "Probabilistic Event Based Rainfall-Runoff Modeling Using Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3799-3814, September.
    2. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
    3. 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.
    4. Mayank Suman & Rajib Maity, 2019. "Assessment of Streamflow Variability with Upgraded HydroClimatic Conceptual Streamflow Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1367-1382, March.
    5. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    6. Saeid Mehdizadeh & Ali Kozekalani Sales, 2018. "A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(9), pages 3001-3022, July.

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