PMID- 28342339
OWN - NLM
STAT- MEDLINE
DCOM- 20171116
LR  - 20181202
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 196
DP  - 2017 Jul 1
TI  - A novel hybrid strategy for PM(2.5) concentration analysis and prediction.
PG  - 443-457
LID - S0301-4797(17)30261-X [pii]
LID - 10.1016/j.jenvman.2017.03.046 [doi]
AB  - 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 (PM(2.5)) 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 PM(2.5) concentration. 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.
CI  - Copyright (c) 2017 Elsevier Ltd. All rights reserved.
FAU - Jiang, Ping
AU  - Jiang P
AD  - School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, 
      China.
FAU - Dong, Qingli
AU  - Dong Q
AD  - School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, 
      China. Electronic address: isaacdon525@aliyun.com.
FAU - Li, Peizhi
AU  - Li P
AD  - School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20170322
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
RN  - 0 (Air Pollutants)
RN  - 0 (Particulate Matter)
SB  - IM
MH  - Air Pollutants
MH  - *Air Pollution
MH  - China
MH  - *Environmental Monitoring
MH  - Particulate Matter
OTO - NOTNLM
OT  - Adaptive fuzzy neural network
OT  - High-dimension association rules
OT  - Learning vector quantization
OT  - Particle matter
OT  - Prediction
EDAT- 2017/03/28 06:00
MHDA- 2017/11/29 06:00
CRDT- 2017/03/26 06:00
PHST- 2016/05/03 00:00 [received]
PHST- 2017/03/11 00:00 [revised]
PHST- 2017/03/16 00:00 [accepted]
PHST- 2017/03/28 06:00 [pubmed]
PHST- 2017/11/29 06:00 [medline]
PHST- 2017/03/26 06:00 [entrez]
AID - S0301-4797(17)30261-X [pii]
AID - 10.1016/j.jenvman.2017.03.046 [doi]
PST - ppublish
SO  - J Environ Manage. 2017 Jul 1;196:443-457. doi: 10.1016/j.jenvman.2017.03.046. 
      Epub 2017 Mar 22.