Abstract
In Meteorological field, where a huge database takes place; weather prediction is a vital process as it affects people's daily life. In the last century, the accuracy of weather predictions has been one of the most challenging concern facing meteorologists around the world. Atmospheric dust is considered to be a harmful air pollutant causing respiratory diseases and infections from one side as well as affecting the earth's energy budget from the other side, so an early prediction of dust phenomena occurrence can be very useful in reducing its harmful effects. Data mining is mainly a machine learning process for extracting useful information form extremely large data base as it is capable of handling huge, noisy, ambiguous, random and missing data, so it represents a very helpful tool in predicting different weather elements. The virtue of using data mining techniques is that they not only analyse the huge historical data base, but also learn from it for future predictions. In this work, we investigate the use of data mining techniques in forecasting different atmospheric phenomena specially atmospheric dust using Decision Tree, k-NN and Naive biased algorithms as well as making a comparison between them by evaluating each model results. The proposed models are implemented using the open source data mining tool Rapidminer.