Rainfall-Runoff Modeling by Using Hybrid System of Artificial Neural Network and Genetic Algorithm


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

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