Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Abstract

Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic. The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists. This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data. We tested the system on the two large real data sets from the areas of central China and Victoria of Australia. The experimental results show that the forecasting rules discovered by the system are very competitive to human experts. The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively.