PM2.5 prediction method using back propagation neural network

The effects of air pollution, amongst the greatest problems facing our planet, are felt throughout all spheres of society, including the transportation sector. Its impacts can range from increased risk of illness to rising temperatures. One of the essential situations for improving inner-city general health and assisting in the creation of a sustainable environment is the ability to forecast air pollution concentrations with accuracy and effectiveness. The backpropagation model is employed in this research to predict the future concentration levels of PM2.5. Data on air quality are gathered and used in the experiment. Empirical Wavelet Transform (EWT) is used to break down the air quality data, which is then utilized to train and evaluate the model. 30% of the data collected is utilized during testing, and 70% was used to train the BP model. The evaluation criteria are applied after testing to determine the model's correctness. The Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE) are the evaluation criteria used and their values were 0.0896, 0.8112, and 0.1162, respectively.