The data of current PM2.5 model forecasting greatly deviate from the measured concentration. In order to solve this problem, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) are combined to build a rolling forecasting model. The important parameters (C and γ) of SVM are optimized by PSO. The data (from February to July in 2015), consisting of measured PM2.5 concentration, PM2.5 model forecasting concentration and five main model forecasting meteorological factors, are provided by Shanghai Meteorological Bureau in Pudong New Area. The rolling model is used to forecast hourly PM2.5 concentration in 12 hours in advance and the nighttime average concentration (mean value from 9 pm to next day 8 am) during the upcoming day. The training data and the optimal parameters of SVM model are different in every forecasting, that is to say, different models (dynamic models) are built in every forecasting. SVM model is compared with Radical Basis Function Neural Network (RBFNN), Multi-variable Linear Regression (MLR) and WRF-CHEM. Experimental results show that the proposed model improves the forecasting accuracy of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day. SVM model performs better than MLR, RBFNN and WRF-CHEM. SVM model greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance, according with the result concluded from previous research. The rolling forecasting model can be applied to the field of PM2.5 concentration forecasting, and can offer help to meteorological administration in PM2.5 concentration monitoring and forecasting.

Date Published: 25 October 2016
PDF: 8 pages
Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101561I (25 October 2016); doi: 10.1117/12.2247134

Published in SPIE Proceedings Vol. 10156:
Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology