Abstract
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.
Highlights
Modeling of rainfall-runoff (r-r) processes performed by hydrologists can be helpful in obtaining information for environmental planning, flooding, and water resources management
The proposed wavelet-emotional artificial neural network (WEANN) was applied to simulate the r-r process of two watersheds, West Nishnabotna River, a subbasin of the Missouri River and Trinity River, a sub-basin in California, United States
From the mathematical point of view, these dynamic coefficients are activated in abnormal conditions and impact and magnify the weights of emotional ANN (EANN). Combining this feature with wavelet transform meant that the performance of WEANN in prediction of peak values was better than EANN (20%) and WANN (30%)
Summary
Modeling of rainfall-runoff (r-r) processes performed by hydrologists can be helpful in obtaining information for environmental planning, flooding, and water resources management. The artificial neural network (ANN), known as a self-learning and self-adaptive function approximator, has been widely used for modeling non-linear hydrological time series due to benefiting from the black box feature (no requirement of prior knowledge), applying a non-linear function to handle the non-linear properties of the process and the ability of analyzing multivariate inputs with different characteristics.
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