Forecasting of Maximum and Minimum Temperature in Mandalay by Evolving Artificial Neural Networks Using Genetic Algorithms

Abstract

Forecasting of weather is very popular in nowadays. But forecasting the future from the observed past is very difficult. There are several forecasting methods for weather data. Among them, evolving artificial neural networks are suitable for weather time series forecasting because of their abilities to learn and adapt a new situation by recognizing new patterns in previous data. However, ANNs present some drawbacks such as over fitting and long time processing. Using ANNs together with genetic algorithm comes to solutions of these problems. Genetic artificial neural networks (GANNs) can give optimal forecasting result from the observed past. In order to provide more effective ANNs, the proposed system use cascade back propagation instead of back propagation method. Weather parameters (attributes) such as rain fall (precipitation), humidity, wind force, dew point, sea level, wind direction will also be used to forecast maximum and minimum temperature.