A Novel Approach to Enhancing Air Pollution Prediction using a Two-Stage Neural XG Boost Detection Algorithm

With the dramatic increase of industry and transportation in modern civilization, air quality monitoring has received a lot of attention. Increased levels of air pollution can hurt the living environment and potentially bring people into harm's way. An accurate and reliable model to forecast air pollutants needs to be developed to reduce air pollution levels and alert the public about upcoming events involving deadly air pollutants. Many researchers have undertaken various strategies to anticipate and reduce air pollution. However, existing prediction systems cannot deliver cost-effective and real-time solutions with sufficient spatial and temporal resolutions of information. In this study, we propose a Two-Stage Neural Extreme Gradient Boost pollution detection (TSN-XGB) algorithm. Initially, we collect the dataset and perform preprocessing using normalization and spatial-temporal feature extraction. We use the proposed Two-Stage Neural Extreme Gradient Boost pollution detection (TSN-XGB) and Enhanced Linear Adam Optimization (ELAO) for prediction. The MATLAB platform evaluates the performance of the suggested approach and compares it with existing methodologies. The newest pollution detection method based on deep learning used an effective proposed algorithm and achieved 93% accuracy.