Suspended sediment load (SSL) time series have three principal inherent components (autoregressive trend, seasonality and stochastic terms) and the overall performance of an SSL modeling tool is associated with the correct estimation of these components. In this study, novel developments of Artificial Neural Network (ANN) models, Emotional-ANN (EANN) and hybrid Wavelet-EANN (WEANN), are employed to estimate the daily and monthly SSL of two rivers (Upper Rio Grande and Lighvanchai) with different hydro-geomorphological conditions. The overall results obtained via autoregressive models, the ANN and EANN, specify the supremacy of EANN (with a few hormonal parameters) against ANN due to the EANN better training the model versus extreme conditions. Also, the obtained results exhibit that the WEANN model could improve the SSL modeling up to 42% and 14% for daily modeling and up to 141% and 87% for monthly modeling in the Upper Rio Grande and Lighvanchai Rivers, respectively.

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