Modeling Air Pollution Levels Using Integrated Weather Stations and Attention Layer Network Methods

Weather factors are influenced by air pollution modeling, which is a major challenge. Consequently, local weather variables must be considered when predicting pollution levels. To model air pollution levels, this research will combine pollution and weather data. The deep learning component that gives rise to the concept of attention is the Attention Layer Network (ALN). The Attention Layer has become very important because of its proven empirical efficacy in modeling sequences, such as language. This approach works well for modeling. Therefore, by utilizing information from the Internet of Things-based weather stations, this research will predict air pollution levels using the ALN approach. These findings demonstrate the high level of accuracy of the ALN model which can predict several environmental conditions. These findings demonstrate the high level of accuracy of the ALN model which can predict several environmental conditions. The research results show minimum prediction error and good performance, with the resulting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}$</tex> value close to 1 and low RMSE, MAE and MAPE values. Improved prediction performance is the result of the implementation of the Attention Layer, which sharpens the emphasis on key elements of incoming data.