Discrete Wavelet Transform and Ensemble Tree Algorithms for Air Pollutant Modeling: A Case Study

Abstract

Air pollution is one of the greatest environmental problems of our time on a global and local scale. Elevated and even low but constant levels of harmful emissions in the air in urbanized areas pose serious risks to the health of the population. This paper develops a new approach for statistical modeling of time series of air pollutants, depending on meteorological factors. A new framework based on discrete wavelet transform (DWT) is proposed for decomposing pollutant's time series as a sum of components that represent trend, seasonality, and other specific characteristics. A key element in the applied DWT is an adaptive scheme for selecting the threshold value to control the reverse DWT's accuracy for achieving better prediction of the time series values. The resulting components are modeled with cutting-edge predictive ensemble tree algorithms, including bagging, boosting, and stacking techniques. This approach is tested with real data measured with a mobile automated station in the Plovdiv region, Bulgaria. All models are evaluated, analyzed, and cross-validated. The models are applied for short-term pollution forecasts.