Climate Change Prediction Using Data Mining

Great progress has been made in the effort to understand and predict El Nino, the anomalous warming of the sea surface temperature (SST) along the equator off the coast of South America which has a strong impact on the climate change over the world. Advances in improved climate predictions will result in significantly enhanced economic opportunities, particularly for the national agriculture, fishing, forestry and energy sectors, as well as social benefits. This paper presents monthly El Nino phenomena prediction using artificial neural networks (ANN). The procedure addresses the preprocessing of input data, the definition of model architecture and the strategy of the learning process. The most important result of this paper is finding out the best model architecture for long term prediction of climate change. Also, an error model has been developed to improve the results.