A novel multi-factor & multi-scale method for PM2.5 concentration forecasting.

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM2.5 concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM2.5-related factors, a novel multi-factor & multi-scale method is proposed for PM2.5 forecasting. Three major steps are taken: (1) multi-factor analysis, to select predictive factors via statistical tests; (2) multi-scale analysis, to extract scale-aligned components via multivariate empirical mode decomposition; and (3) PM2.5 prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques (without multi-factor and multi-scale analysis), semi-improved variants (with either multi-factor or multi-scale analysis), and similar counterparts (with other multi-scale analyses) in terms of prediction accuracy.

[1]  Yu Jin,et al.  The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model , 2018, Inf. Sci..

[2]  Jin Guo,et al.  PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing, China , 2015, PloS one.

[3]  Hong Huang,et al.  Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data , 2017 .

[4]  Deborah J. Luecken,et al.  Development and analysis of air quality modeling simulations for hazardous air pollutants , 2006 .

[5]  Haixiang Guo,et al.  Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM 10 forecasting , 2018 .

[6]  Feng Xu,et al.  Prediction of hourly PM 2.5 using a space-time support vector regression model , 2018 .

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Huanfeng Shen,et al.  The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations , 2017, International journal of environmental research and public health.

[9]  Ping-Huan Kuo,et al.  A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities , 2018, Sensors.

[10]  Jiachen Zhao,et al.  Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. , 2019, Chemosphere.

[11]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[12]  Sachit Mahajan,et al.  Improving the Accuracy and Efficiency of PM2.5 Forecast Service Using Cluster-Based Hybrid Neural Network Model , 2018, IEEE Access.

[13]  Ling Tang,et al.  A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting , 2015, Int. J. Inf. Technol. Decis. Mak..

[14]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[15]  Shaolong Sun,et al.  Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM2.5 concentration forecasting. , 2017, Journal of environmental management.

[16]  Yufang Wang,et al.  A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting , 2016 .

[17]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[18]  Ping Jiang,et al.  A novel hybrid strategy for PM2.5 concentration analysis and prediction. , 2017, Journal of environmental management.

[19]  R. Srivastava,et al.  Evaluation of environmental impacts of Integrated Industrial Estate—Pantnagar through application of air and water quality indices , 2011, Environmental monitoring and assessment.

[20]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[21]  Lean Yu,et al.  Why do EMD‐based methods improve prediction? A multiscale complexity perspective , 2019, Journal of Forecasting.

[22]  Zhongfei Zhang,et al.  Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality , 2017, IEEE Transactions on Knowledge and Data Engineering.

[23]  Yongqiang Liu,et al.  Important meteorological variables for statistical long-term air quality prediction in eastern China , 2018, Theoretical and Applied Climatology.

[24]  Olivier Grunder,et al.  A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine. , 2017, The Science of the total environment.

[25]  Xiaobo Zhang,et al.  Developing an early-warning system for air quality prediction and assessment of cities in China , 2017, Expert Syst. Appl..

[26]  Jingjing Xie,et al.  A multi-scale relevance vector regression approach for daily urban water demand forecasting , 2014 .

[27]  Hongbo Fu,et al.  Formation, features and controlling strategies of severe haze-fog pollutions in China. , 2017, The Science of the total environment.

[28]  Deyun Wang,et al.  Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution , 2017, International journal of environmental research and public health.

[29]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[30]  Yuanyuan Wang,et al.  Daily air quality index forecasting with hybrid models: A case in China. , 2017, Environmental pollution.

[31]  Jianzhou Wang,et al.  Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China. , 2017, Environmental pollution.

[32]  Ling Yang,et al.  PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors , 2018, Atmospheric Environment.

[33]  Jingfeng Huang,et al.  A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China , 2014 .

[34]  Liang Zhai,et al.  An improved geographically weighted regression model for PM 2.5 concentration estimation in large areas , 2018 .

[35]  S. Adarsh.  Finer Scale Rainfall Projections for Kerala Meteorological Subdivision, India Based on Multivariate Empirical Mode Decomposition , 2016 .

[36]  Jianzhou Wang,et al.  A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network. , 2014, The Science of the total environment.

[37]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[38]  D. Qin,et al.  Analyses of regional pollution and transportation of PM2.5 and ozone in the city clusters of Sichuan Basin, China , 2019, Atmospheric Pollution Research.

[39]  C. Chatfield,et al.  Fourier Analysis of Time Series: An Introduction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[40]  Yanhui Chen,et al.  Price forecasting in the precious metal market: A multivariate EMD denoising approach , 2017 .

[41]  Xiaodao Chen,et al.  PM2.5 forecasting with hybrid LSE model‐based approach , 2017, Softw. Pract. Exp..

[42]  Ming Li,et al.  Forecasting Fine-Grained Air Quality Based on Big Data , 2015, KDD.

[43]  Yunzhen Xu,et al.  Air quality early-warning system for cities in China , 2017 .

[44]  M. G. Estes,et al.  Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression. , 2013, Environmental research.

[45]  Ling Tang,et al.  A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting , 2017, Appl. Soft Comput..

[46]  Wenbin Sun,et al.  Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing–Tianjin–Hebei, China , 2019, Atmospheric Environment.

[47]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[48]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[49]  Xiang Li,et al.  Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. , 2017, Environmental pollution.

[50]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[51]  Jen-Wei Huang,et al.  Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations , 2018, IEEE Access.

[52]  Jiuchang Wei,et al.  Public attention to the great smog event: a case study of the 2013 smog event in Harbin, China , 2017, Natural Hazards.

[53]  Li Li,et al.  Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China , 2015, International journal of environmental research and public health.

[54]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[55]  Martha Cobo,et al.  Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering , 2018, Atmospheric Pollution Research.

[56]  Aranildo R. Lima,et al.  Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods , 2017, Air Quality, Atmosphere & Health.

[57]  Ziyue Chen,et al.  Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region , 2017, Scientific Reports.

[58]  Alexis K.H. Lau,et al.  Time Series Forecasting of Air Quality Based On Regional Numerical Modeling in Hong Kong , 2018 .

[59]  W. Staszewski.  IDENTIFICATION OF NON-LINEAR SYSTEMS USING MULTI-SCALE RIDGES AND SKELETONS OF THE WAVELET TRANSFORM , 1998 .

[60]  M. Kolehmainen,et al.  Neural networks and periodic components used in air quality forecasting , 2001 .

[61]  Qiang Zhang,et al.  Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014-2016. , 2019, Journal of environmental sciences.

[62]  Qinghua Hu,et al.  Ultra-short-term wind speed prediction based on multi-scale predictability analysis , 2016, Cluster Computing.

[63]  Lean Yu,et al.  A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting , 2018, Energy.