  We propose a didactic approach to use the Machine Learning protocol in order
to perform weather forecast. This study is motivated by the possibility to
apply this method to predict weather conditions in proximity of the Etna and
Stromboli volcanic areas, located in Sicily (south Italy). Here the complex
orography may significantly influence the weather conditions due to Stau and
Foehn effects, with possible impact on the air traffic of the nearby Catania
and Reggio Calabria airports. We first introduce a simple thermodynamic
approach, suited to provide information on temperature and pressure when the
Stau and Foehn effect takes place. In order to gain information to the rainfall
accumulation, the Machine Learning approach is presented: according to this
protocol, the model is able to ``learn'' from a set of input data which are the
meteorological conditions (in our case dry, light rain, moderate rain and heavy
rain) associated to the rainfall, measured in mm. We observe that, since in the
input dataset provided by the Salina weather station the dry condition was the
most common, the algorithm is very accurate in predicting it. Further
improvements can be obtained by increasing the number of considered weather
stations and time interval.
