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Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process

Author

Listed:
  • Elnaz Sharghi

    (Univ. of Tabriz)

  • Vahid Nourani

    (Univ. of Tabriz
    Near East University)

  • Hessam Najafi

    (Univ. of Tabriz)

  • Amir Molajou

    (Iran University of Science & Technology)

Abstract

The hydrological time series have three principle components (autoregressive, seasonality and trend) and the performance of the models is strongly related to the nature of these components. The current research examines the accuracy of two Artificial Neural Network (ANN) based approaches for rainfall-runoff (r-r) modeling of two catchments with different geomorphological conditions at monthly and daily time scales. The techniques proposed here are hybrid wavelet-ANN (WANN) model, as a multi-resolution forecasting tool and Emotional Artificial Neural Network (EANN) (a new generation of ANN based models) which serves artificial emotional factors as well as classic bias and weights parameters. The obtained results for monthly modeling show that WANN could perform better than the simple feed forward neural network (FFNN) model up to 40% and 35% in terms of verification and training efficiency criteria due to significant seasonality involved in the monthly time series of the process. On the other hand, the obtained results for daily modeling via FFNN and EANN, both as Markovian models, indicates the superiority of EANN over FFNN because of EANN capability to better learning of extraordinary and extreme conditions of the process in the training phase.

Suggested Citation

  • Elnaz Sharghi & Vahid Nourani & Hessam Najafi & Amir Molajou, 2018. "Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3441-3456, August.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:10:d:10.1007_s11269-018-2000-y
    DOI: 10.1007/s11269-018-2000-y
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    References listed on IDEAS

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    1. Yan-Fang Sang, 2013. "Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2807-2821, June.
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