Training back propagation neural networks with genetic algorithm for weather forecasting

Accurate weather forecasting is important in today´s world as agricultural and industrial sectors are largely dependent on the weather conditions. Secondly, it is used to warn about natural disasters. Due to non-linearity in climatic physics, neural networks are suitable to predict these meteorological processes. Back Propagation algorithm using gradient descent method is the most important algorithm to train a neural network for weather forecasting. Back propagation algorithm suffers from several problems. In this paper, in order to overcome some of these problems, an integrated back propagation based genetic algorithm technique to train artificial neural networks is proposed. In the proposed technique, back propagation is combined with genetic algorithm in such a way that the pitfalls of the algorithm get converted to benefits. The results and comparison of the technique with the previous one are enlisted along with.