Rainfall-Runoff Modeling by Using Hybrid System of Artificial Neural Network and Genetic Algorithm
published by Aِl-Baath University
in 2017
in
and research's language is
العربية
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Abstract in English
This study has reached to that ANN (5-9-1) (five neurons in input
layer_nine neurons in hidden layer _ one neuron in output layer) is the
optimum artificial network that hybrid system has reached to it with
mean squared error equals (1*10^-4) (0.7 m3/sec), where this software
has summed up millions of experiments in one step and in limited time, it
has also given a zero value of a number of network connections, such as
some connections related of relative humidity input because of the lake
of impact this parameter on the runoff when other parameters are
avaliable.
This study recommend to use this technique in forecasting of
evaporation and other climatic elements.
References used
AWAD, A. ؛POSER, I. 2007-Calibrating Conceptual Rainfall- Runoff Models Using a Real Genetic Algorithm Combined with a Local Search Method, Vol. 1, 174-181
Mutlu, E; Chaubey, I; Hexmoor, H; Bajwa, S. 2008- Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed, Published online in Wiley InterScience, 1-10
ASADI, S.؛ SHAHRABI, J.؛ ABBASZADEH, P. ؛TABANMEHR, S. 2013- A new hybrid artificial neural networks for rainfall_runoff process modeling, Neurocomputing an international journal, Iran, 470_480