# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 6 Conclusions and Future Research Directions  
This study presents the use of an Artificial Neural Network with a Multilayer Perceptron (MLP) architecture regressive model to forecast selected weather parameters. The MLP model was built, and its effectiveness was compared with the forecasts available in the website archive. The developed model is a lightweight solution and can be an element of any application that would require forecasting of the above weather parameters. The most important aims achieved by us are listed below:  
* This study presents a successful attempt to use an application based on a model of a unidirectional multilayer Artificial Neural Network to forecast selected weather conditions for a selected location, i.e., the city of Szczecin. * The application used in this survey was successful for a local scale study. * We obtained satisfactory results, i.e., accurate forecasts, with the simple design of the MLP model, which comprised three layers of neurons. * We confirmed that this forecasting model was properly designed and implemented, that this is a model suitable for forecasting selected weather parameter, and that the data used to train the model were properly selected and prepared. * We analyzed and explained the reasons for the different forecasting accuracy results for the different weather parameters. Our approach in this study was limited to using data and performing forecasts of the selected weather conditions for the local area of Szczecin city in Poland. The weather data set limited to the territory of the city of Szczecin was a small set. As our study was focused on a regional territory and dataset, the usefulness for a larger area would be limited. The achieved results encourage future investigations to generalize whether the satisfactory scores obtained by this application working on local data will be repeatable and useful in the case of other regions of Poland. The next direction of further work is to compare the performance of this application for a larger dataset including Poland and the European area. It would also be desirable for the direction of future work to include comparing the effectiveness of our method with applications working on other, newer, and more advanced machine learning models to see if the results obtained with our proposed method working in a weather forecasting application can be improved. The MLP provided slightly more accurate predictions of temperature and atmospheric pressure compared with the LSTM and SVR. This indicates that an appropriate choice of data set, learning features, and parameters for the MLP training can provide results comparable to more complex ML models. For the wind speed prediction, LSTM and SVR showed a slight advantage, and for the precipitation sum prediction, LSTM. Thus, we concluded that, for weather conditions that are more difficult to predict, more complex ML models, especially LSTM, represent an opportunity to obtain more accurate predictions, and it is worthwhile to focus on research aimed at adjusting their structure to further improve the prediction performance. As the MLP model did not achieve equally effective results for all the predicted weather conditions, further work is needed to improve the accuracy and expand the model. To improve the accuracy of the prediction of the weather parameters with the MLP model, more hidden layers in the network model can be used, i.e., a deep network model can be used because the quality of the multilayer network is higher compared to traditional simple MLP networks with only a few hidden layers. For more accurate forecasts, the precision of the information provided by the training data can be increased by using more densely spaced samples in the training dataset, e.g., every hour instead of every 24 h.  
To obtain more accurate forecasts, more training samples can be used for the day that the weather parameters are forecast, e.g., as a training sample of data from ten days ago.