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Deadlines
(Previous Conference: ESG 2017, Athens, Greece, April 9-11 2017)
PLENARY SPEAKERS:

Prof. Vahid Nourani, University of Tabriz, IRAN, e-mail: nourani@tabrizu.ac.ir
Title: "Multistep Modeling of Hydroclimatic Phenomena Using Wavelet-Neural Network Seasonal Model"
Abstract: Hydroclimatic models play an important role in management of water resources, flood control and forecast the flow discharge in rivers. Considering that the Hydroclimatic time series have three principle component: autoregressive, seasonality, stochastic and the difference behavior of the models relative to this components, in this research, monthly rainfall and runoff data of Australia Murrumbidgee catchment and monthly minimum temperature data of Tabriz have been used to develop and evaluate the combined wavelet-neural network model. Considering that the ability of artificial neural network (ANN) model has been proved in hydroclimatic processes one step ahead modeling, in the present paper, the ability of ANN and combined wavelet-neural network (WANN) model were investigated to multistep modeling of hydroclimatic processes with the least input. For this purpose, the ANN model and then WANN model were used to predict one to twelve next steps. Finally, the efficiencies of all models were examined using the evaluation criteria and all models were compared with each other. The modeling results indicated that the use of wavelet transform as a preprocessing of data has been caused considerably increasing the accuracy of all prediction steps.