A novel hybrid strategy for PM2.5concentration analysis and prediction.
The analysis and prediction of air pollutants are of great significance in environmental research today since airborne pollution is a substantial threat, especially in urban agglomerations of China. To develop more effective warning systems and management advice, the authorities and city dwellers need more accurate forecasts of the air pollution. Most previous analysis systems were based on costly observation apparatus at fixed sites, forecasting models were usually built on observations within a certain range, and some observations contained biases. In this paper, a novel and effective framework, termed HML-AFNN, was successfully developed to analyse and forecast the concentration of particular matter (PM2.5) for a selected number of forward time steps. In a simulation of the trajectory of air pollutants, the high-dimension association rules (HDAR) approach considered the tempo-spatial relations, as well as the meteorological and geographical factors of the ambient regions, as parameters. In addition, the learning vector quantization (LVQ) network was adopted to select the appropriate inputs to improve the efficiency of the training process. Moreover, an adaptive fuzzy neural network (AFNN), a combination of neural and fuzzy logic, was utilized to analyse and predict the PM2.5concentration. The experiment results of our study on two major urban agglomerations of China, the Jing-Jin-Ji area and Pearl River Delta, over a period of more than one year demonstrated that the developed hybrid HML-AFNN model outperforms a plain AFNN, an HM-AFNN model without LVQ and the least squares support vector machines (LS-SVM); this superior performance can be determined from the values of several error indexes, including MAE, MAPE and band errors. This hybrid model, which has robust and accurate results, shows the potential to be a political and administrative method to issue effective early warnings and to design suitable abatement strategies.
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
Ping
P
Jiang
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China. Electronic address: isaacdon525@aliyun.com.
Qingli
Q
Dong
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
Peizhi
P
Li
D000393:Air Pollutants; D000397:Air Pollution; D002681:China; D004784:Environmental Monitoring; D052638:Particulate Matter
D000393:Air Pollutants; D052638:Particulate Matter
Adaptive fuzzy neural network; High-dimension association rules; Learning vector quantization; Particle matter; Prediction