Proposal of a Modified Loss Function with the Gaussian Copula Density Function to Improve LSTM Predictions of PM10 and PM2.5 Concentrations

Air pollution forecasting for Particulate Matter (PM10 and PM2.5) is a challenge for human health in order to improve the life quality of humans around the world. This research focuses on evaluating a Long Short-Term Memory (LSTM) neural network model with an improvement in the loss function using the Gaussian Copula Density function to predict PM10 and PM2.5 levels in four stations (AJM, CAM, MER and PED) in Mexico City. The model is compared with a plain LSTM neural network model for forecasting 12, 24, 48 and 72 h using error metrics root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results demonstrate a superior performance of the modified loss function model, achieving the lowest error values across multiple stations and forecast horizons.