<![CDATA[Dam discharge forecasting is needed to plan water allocation plans for various needs such as for Hydropower plant, flood control and irrigation. Artificial neural network in this case backpropagation method has a learning method to change the weight of the value of the architecture of the artificial neural network.#Genetic algorithms can optimize the#weight of artificial neural networks to avoid the occurrence of a minimum local which is a weakness of backpropagation. Genetic algorithms will optimize the weight#of the artificial neural network so individuals which are produced as a weight representation with the best fitness value resulting from the optimization process with the genetic algorithm then used as the initial weight of the artificial neural network backpropagation method. The data used as input data is the dam discharge time series data the previous months. The data used is monthly debit data from 2008 to 2017. Input data will be processed to produce an output value which is the forecasted value of the dam discharge in the next month. The optimal training parameters for genetic algorithm and backpropagation training are the population size=100, the generation=100, Cr and Mr combination 0,6 and 0,4, the number of iterations=500, the value of learning rate=0,7. The test results using optimal parameters get the MSE value=0,04188]]>
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