Multi-model Ensemble Forecast of PM2.5 Concentration Based on the Improved Wavelet Neural Networks

摘要

Concerning the uncertainty problem of different environment meteorological models and improving the accuracy of PM2.5 concentration forecast, in this article, an improved wavelet neural network ensemble algorithm with additional momentum item is proposed and the forecast products of the three environment meteorological models including China Meteorological Administration Unified Atmospheric Chemistry Environment, Beijing Regional Environmental Meteorology Prediction System and Weather Research Forecasting/Chemistry are integrated. The multi-model ensemble rolling forecasting model of PM2.5 concentration is established. The experiment is carried out by data of Beijing station, and the forecast results are compared with seven other machine learning algorithm models (Wavelet, BP, RBF, Elman, T-S fuzzy SVR and CNN). The results show that PM2.5 concentration forecasted by the improved wavelet neural network is better than the other models, and the new method reduces the prediction deviation effectively. (C) 2019 Society for Imaging Science and Technology.