Comparative analysis of machine learning algorithms based on an air pollution prediction model

In this paper it has been assumed that the use of artificial intelligence algorithms to predict the level of air quality gives good results. Our goal was to perform a comparative analysis of machine learning algorithms based on an air pollution prediction model. By repeatedly performing tests on a number of models, it was possible to establish both the positive and negative influence of the parameters on the result generated by the ANN model. The research was based on some selected both current and historical data of the air pollution concentration altitude and weather data. The research was carried out with the help of the Python 3 programming language, along with the necessary libraries such as TensorFlow and Jupyter Notebook. The analysis of the results showed that the optimal solution was to use the Long Stort Term Memory LSTM algorithm in smog prediction. It is a recursive model of an artificial neural network that is ideally suited for prediction tasks. Further research on the models may develop in various directions, ranging from increasing the number of trials which would be linked to more reliable data, ending with increasing the number of types of algorithms studied. Developing the models by testing other types of activation and optimization functions would also be able to improve the understanding of how they affect the data presented. A very interesting developmental task may be to focus on a self-learning artificial intelligence algorithm, so that the algorithm can learn on a regular basis, not only on historical data. These studies would contribute significantly to the amount of data collected, its analysis and prediction quality in the future.