Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling

Abstract The current research introduces a combined wavelet-emotional artificial neural network (WEANN) approach for one-time-ahead rainfall-runoff modeling of two watersheds with different geomorphological and land cover conditions at daily and monthly time scales, to utilize within a unique framework the ability of both wavelet transform (to mitigate the effects of non-stationary) and emotional artificial neural network (EANN, to identify and individualize wet and dry conditions by hormonal components of the artificial emotional system). To assess the efficiency of the proposed hybrid model, the model efficiency was also compared with so-called EANN models (as a new generation of ANN-based models) and wavelet-ANN (WANN) models (as a multi-resolution forecasting tool). The obtained results indicated that for daily scale modeling, WEANN outperforms the other models (EANN and WANN). Also, the obtained results for monthly modeling showed that WEANN could outperform the WANN and EANN models up to 17% and 35% in terms of validation and training efficiency criteria, respectively. Also, the obtained results highlighted the capability of the proposed WEANN approach to better learning of extraordinary and extreme conditions of the process in the training phase.

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