An approach for multiparameter meteorological forecasts

Abstract

Accurate meteorological forecasting has great importance in different fields. This works introduces a system to obtain precise predictions, which uses regression functions, and collected data using the meteorological stations from the Gran Canaria and South Tenerife airports. The dataset offers information about different phenomena as temperature, wind speed, solar radiation, pressure, moisture, cloudiness, rainfall and meteors. A preprocessing stage has been applied before prediction stage to adapt the collected data. A support vector machine, regression tree, and fit linear model are applied as regression functions. Results has been measured by the mean square error. These results reached an accuracy of 0.07 °C for temperature, 0.56 km/h for wind speed, 7.45 tenths of kJ/m2 for solar radiation and 0.11 mm for precipitation. It shows the robustness of the multiparameter meteorological forecast approach.