An Optimized Hybrid Framework for Air Quality Index Prediction

Vehicles are the main cause of air pollution because they release substances such as lead ions directly into the air, thereby causing a number of secondary pollutants which are formed through the biochemical reactions. In some circumstances, the air could even change the course of events when it gets highly polluted. The proposed framework's effort is an attempt to use processes to create a new model for forecasting and predicting air pollution, which is blending of three different models that are Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and Gaussian Process Regression (GPR). In a data where the temporal dependencies can occur, LSTM networks work especially well for time series prediction and can, therefore, identify the patterns that are sequential in air quality data. Non-linear modelling ability improved by SVM tackles the complexities of interactions of the different air contaminants as supplementary to the LSTM technique. GPR enables forecasts that are quantifiable in terms of their uncertainty by the very nature of its probabilistic character, which undoubtedly is invaluable for management of air quality index prediction. This combination technique gives AQI forecasts that are both more precise and reliable than just single model operations. This integrated model contributes a strong predictive tool to the field of air quality management and shows great promise for real-time applications in environmental monitoring and public health policy. The applicability of this model to different geographical areas and pollution circumstances may be expanded in future research.