# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 5 Discussion  
The study described in this report aimed to show the possibility of using a unidirectional multilayer neural network to forecast selected weather indicators and to compare the results achieved with other forecasting models. We assumed that, with a reliable and sufficiently large set of weather data, a properly constructed neural network model, and appropriate software, it would be possible to effectively forecast selected weather indicators. The results of an application using ANN as presented in this report confirm that Artificial Neural Networks can be useful as a tool for forecasting weather indicators. Although a simple construction of the MLP model--comprising three layers of neurons--was used in the study, the results obtained were satisfactory. The surprising effectiveness of our implementation of the MLP model with comparable or, in most cases, higher effectiveness compared with the other forecasting models proves that this forecasting model was properly designed and implemented and is a model suitable for the assumed purpose, i.e., forecasting selected weather parameters, and that the data used to train the model were properly prepared. However, there were some difficulties in applying the proposed weather forecasting model. As reported in the literature [18], neural networks can be susceptible to learning false relationships between data. A pure data-based weather forecasting model may fail to respect basic physical principles and, thus, generate false forecasts because it does not take into consideration that every atmospheric process is affected by physical laws. There are specific properties of weather data for which classical ML concepts (which work for typical problems solved by ML, such as computer vision and speech recognition) are not effective enough in a complete weather prediction system. The reason for this is the necessity for the model to handle the complexity of the meteorological data and feedback processes to provide accurate prediction results. Another difficulty encountered when using MLPs for weather forecasting is because ANNs are good interpolators but poor extrapolators. The dataset used to train the ANN must, therefore, contain numerous and heterogeneous examples to cover the widest range of cases that the ANN is expected to predict [34]. A separate training and testing dataset containing regional data would be required for each location where the method would be applied. The training dataset needs to be updated regularly due to occurring climate changes, and the network model needs to be re-trained due to the changing climate patterns in the world [85]. Another disadvantage of MLPs and deep learning models over other methods is that their training process takes a long time [62]. Forecasting time series, which include a prediction of the weather parameters changing over time, is an important area of machine learning. The time component provides useful information that is used in the construction of the machine learning model, but it also brings with it problems that make it difficult to accurately predict certain variables. If a time series' data are correlated over time, it is much easier to obtain an accurate prediction because the model uses historical values in the machine learning process and then generates a forecast for the future from these. When data values change randomly over time, the model cannot predict future changes based on historical events with great accuracy [82]. The implications of this are highly accurate prediction results for weather conditions that show a high correlation with the other variables included in the prediction model and poor results for weather conditions for which no such correlations exist. In most of the analyzed cases, the MLP model achieved higher or comparable results to the forecasts from the internet weather service. The highest prediction efficiency was observed for the maximum and minimum temperature. These are conditions for which there is a strong correlation in time with many other weather indicators.