Design and implementation of local data mining model for short-term fog prediction at the airport

This paper presents a short-term prediction of fog occurrence based on suitable data mining methods. The whole process was implemented through CRISP-DM methodology that represents most commonly used approach for data mining. This methodology consists of six main phases, which we describe in this paper for our application: business understanding, data understanding, data preparation, modeling, evaluation and deployment that resulted into new and useful knowledge to be used in real practice. The main motivation behind our solution was to develop an effective data mining model based on local conditions at the airport for short-term fog prediction as crucial factor for air management. Our first results presented in this paper are promising.