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Short-term Weather Forecasting with Neural Networks

  • Marcin Jaruszewicz
  • Jacek Mańdziuk
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

A method of short-term weather forecasting based on artificial neural networks is presented. Each training sample consist of a date information combined with meteorological data from the last three days gathered at the meteorological station in Miami, USA. The prediction goal is the next day’s temperature. Prediction system is built based on multilayer perceptron network trained with backpropagation algorithm with momentum.

The average prediction error of the network on the test set equals 1.12°C. The average percentage predictio n error is equal to 5.72%. The results are very encouraging and provide a promise for further exploration of the issue. The so-called correlation ratio δ between predicted and real changes (trends) is equal to 0.7136. Relatively high value of δ additionally confirms good quality of presented results.

Experimental results of application of the Principal Component Analysis method at the stage of pre-processing of the input data are also presented. In that case the average prediction error and the average percentage prediction error are equal to 2.76°C and 15.28%, respectively.

Keywords

Artificial Neural Network Weather Prediction Principal Component Analysis Method Average Prediction Error Uncorrelated Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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