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Water Resources Management

, Volume 32, Issue 10, pp 3441–3456 | Cite as

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

  • Elnaz SharghiEmail author
  • Vahid Nourani
  • Hessam Najafi
  • Amir Molajou
Article

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.

Keywords

Rainfall-runoff modeling Seasonality models Autoregressive Emotional Artificial Neural Network (EANN) Wavelet transform 

Notes

Compliance with Ethical Standards

Conflict of the Interest

None.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Water Resources Engineering, Faculty of Civil EngineeringUniv. of TabrizTabrizIran
  2. 2.Department of Civil EngineeringNear East UniversityNicosiaTurkey
  3. 3.Department of Water Resources Engineering, Faculty of Civil EngineeringIran University of Science & TechnologyTehranIran

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