Rainfall Forecast using a Neural Network with a Real-coded Genetical Preprocessing

S. Ito, Y. Mistukura, and M. Fukumi (Japan)

Keywords

Neural Network, Real-Coded GA, Rainfall Forecast

Abstract

In this paper, rainfall is forecasted by 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 rainfall forecast system, computer simulations are performed for real weather data.

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