applied sciences Article An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions Aleksandra B ˛aczkiewicz 1,2 , Jarosław W ˛atróbski 1,* , Wojciech Sałabun 3 and Joanna Kołodziejczyk 3 1 Institute of Management, University of Szczecin, Cukrowa 8, 71-004 Szczecin, Poland;
[email protected] 2 Doctoral School of University of Szczecin, Mickiewicza 16, 70-383 Szczecin, Poland 3 Research Team on Intelligent Decision Support Systems, Department of Artificial Intelligence and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin ul. Zołnierska˙ 49, 71-210 Szczecin, Poland;
[email protected] (W.S.);
[email protected] (J.K.) * Correspondence:
[email protected] Abstract: Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its Citation: B ˛aczkiewicz,A.; effectiveness on real data. The principal aim of this study is to present the use of a regressive model W ˛atróbski,J.; Sałabun, W.; based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to Kołodziejczyk, J.