Feature Data Selection for Rainfall Forecast by Using Real-Coded GA

S. Ito, M. Fukumi, Y. Mitsukura, and N. Akamatsu (Japan)


Neural Network, Real-Coded GA, Rainfall Forecast


In this paper, rainfall is forecasted by using a Neural Network (NN) and a Genetic Algorithm (GA). GA selects data needed to predict the rainfall. NN learns and forecasts it using attributes selected by GA. The real-coded GA is used to decide data priority degree, and data really needed for the rainfall forecast is selected based on the priority. Finally, in order to show the effectiveness of the proposed fainfall forecast system, computer simulations are performned for real weather data.

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