# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 2 Literature Review  
Our literature review contains studies of machine learning methods and their applicability to weather data along with their relevant statistical properties. Numerical Weather Prediction (NWP) models play a key role in operational weather forecasting, especially for longer prediction times. Utilizing NWP with deep learning to improve the accuracy of weather forecasting systems is a fruitful avenue to consider. An article published in 2020 presents results from the Ensemble of Spatial-Temporal Attention Network and Multi-Layer Perceptron (E-STAN-MLP) for forecasting meteorological elements to predict the surface temperature, humidity, wind speed, and direction at 24 automatic meteorological stations in Beijing. This research can be generalized to local weather prediction in other regions [29]. Multilayer Perceptrons (MLPs) belong to the common type of feed-forward networks used for the future predictions of rainfall and temperature [30]. MLP has been continuously used for many years to solve problems, such as the prediction of weather conditions. MLP is important for real-life problems [31]. In the study described in the article, in 2020, it was used as a predictive model for wind speed forecasting (wind power) in Villonaco alongside a Long Short-Term Memory (LSTM) and Convolutional Neural Network model (CNN) [32]. The continued popularity and usefulness of the MLP is also demonstrated by its use as a prediction model for time series of meteorological tsunamis next to Evolved Radial Basis Function (ERBF) in Evolved Neural Networks (ENN) in 2020 for Accurate Meteorological Forecasting Applications in Vietnam [33]. In a paper published in 2021, [PERSON] and others presented an application of the ANN technique in the form of an MLP model as a tool to correct and complement a numerical model for forecasting the wind speed and direction at the local scale, including the Cadarache valley, which is localized in southeastern France. As a measure of network accuracy, the Mean Absolute Error (MAE) was taken [34]. The satisfactory results obtained by the authors confirmed the usefulness of this tool in weather condition prediction. A Multilayer Perceptron has been successfully used as a predictive model for various natural conditions, such as soil temperature [35], compared to the Support Vector Machine (SVM) for rainfall prediction [36; 37]. This recent literature showed that the use of a Multilayer Perceptron for predicting weather conditions was confirmed and, thus, indicated the possibility of its further use. To research weather condition predictions performed in Poland and worldwide as well as the methods used, we reviewed the literature. The subject of the work by [PERSON] and [PERSON] from 2007 was the application of an Artificial Neural Network (Multilayer Perceptron) in forecasting the mean temperature for the next day for Kermanshah in Iran [15]. The authors of the study trained and tested the MLP model using previous meteorological data. The chosen weather data were divided into two randomly selected groups: the training group, corresponding to 67% of the patterns, and the test group, corresponding to 33% of the patterns. \(MAE\) was used as a measure of the network accuracy. The training data were selected as the mean temperature, wind speed, humidity, pressure, and sunshine. The dataset was normalized by converting its values to a range between \(-1\) and \(1\). In a paper from 2009, [PERSON] and [PERSON] projected the maximum temperature for the next day [38]. The MLP prediction was compared with SVM. For MLP, an algorithm of backpropagation was used. A training dataset from 5 years and test data from 1 year were used. In the case of SVM, the Radial Basis Function Kernel (RBF), which is a popular kernel function used in various kernelized learning algorithm functions, was used. There are three layers in the ML: input, hidden, and output, as well as an algorithm for the backpropagation of errors. A sigmoid function was selected as the activation function.