Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives - Iftikhar, Hasnain; Qureshi, Moiz; Zywiołek, Justyna; López-Gonzales, Javier Linkolk; Albalawi, Olayan (Frontiers in Environmental Science, 2024)
Particulate matter with a diameter of 2.5 microns or less (PM 2.5 ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM 2.5