Air Pollutant Concentration Forecasting with WTMP: Wavelet Transform-Based Multilayer Perceptron

Atmospheric pollutants’ real-time changes and the internal interactions among various data make it challenging to efficiently predict concentration variations. In order to extract more information from the time series of pollutants and improve the accuracy of prediction models, we propose a type of Multilayer Perceptron model based on wavelet decomposition, named Wavelet Transform-based Multilayer Perceptron (WTMP) model. This model decomposes pollutant data through overlapping discrete wavelet transforms to extract non-stationarity and nonlinear dependencies in the time series. It combines the decomposed data with static covariate information such as data collection time and inputs them into an improved Multilayer Perceptron (MLP) model, reconstructing and outputting the prediction results. Finally, the model is validated using atmospheric pollutant data collected at a specific location in Ruian City, Zhejiang Province, China. The results indicate that the model performs well with minimal prediction errors.