Daily air quality index forecasting with hybrid models: A case in China.
暂无分享,去创建一个
Yuanyuan Wang | Jianming Hu | Suling Zhu | Haixia Liu | Xiuyuan Lian | Jinxing Che | Jinxing Che | Suling Zhu | Yuanyuan Wang | Xiuyuan Lian | Haixia Liu | Jianming Hu
[1] Wei-Zhen Lu,et al. Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. , 2008, The Science of the total environment.
[2] Jeffrey M. Vukovich,et al. Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies , 2014 .
[3] Yang Zhang,et al. Real-time air quality forecasting, part I: History, techniques, and current status , 2012 .
[4] Ni Sheng,et al. The first official city ranking by air quality in China — A review and analysis , 2016 .
[5] M. Gardner,et al. Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London , 1999 .
[6] Gordon Reikard. Forecasting volcanic air pollution in Hawaii: Tests of time series models , 2012 .
[7] S. Samarasinghe,et al. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering , 2014 .
[8] Hong Zhang,et al. A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting , 2017 .
[9] Yun Zeng,et al. Progress in developing an ANN model for air pollution index forecast , 2004 .
[10] J. Hooyberghs,et al. A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .
[11] S. Chaudhuri,et al. Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models , 2014, Environmental Monitoring and Assessment.
[12] Gilles Foret,et al. Combining deterministic and statistical approaches for PM10 forecasting in Europe , 2009 .
[13] J. Chow,et al. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile , 2008 .
[14] Anikender Kumar,et al. Forecasting of daily air quality index in Delhi. , 2011, The Science of the total environment.
[15] Le Jian,et al. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. , 2012, The Science of the total environment.
[16] 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.
[17] Tzu-Li Tien,et al. A new grey prediction model FGM(1, 1) , 2009, Math. Comput. Model..
[18] Shu-Shen Liu,et al. Support vector regression and least squares support vector regression for hormetic dose-response curves fitting. , 2010, Chemosphere.
[19] Jingjing Xie,et al. Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions , 2016 .
[20] Wei Chen,et al. Urban air quality evaluations under two versions of the national ambient air quality standards of China , 2016 .
[21] Liljana Ferbar Tratar,et al. The comparison of Holt–Winters method and Multiple regression method: A case study , 2016 .
[22] Qi Li,et al. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation , 2015 .
[23] 刘勇,et al. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. , 2015 .
[24] Bijan Yeganeh,et al. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model , 2012 .
[25] W. Geoffrey Cobourn,et al. An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations , 2010 .
[26] George D. C. Cavalcanti,et al. Hybrid intelligent system for air quality forecasting using phase adjustment , 2014, Eng. Appl. Artif. Intell..
[27] S. S. Shen,et al. Applications of Hilbert–Huang transform to non‐stationary financial time series analysis , 2003 .
[28] Feng Liu,et al. The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region , 2015 .
[29] Jorge Reyes,et al. Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile , 2000 .
[30] Zhifu Tao,et al. A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model , 2018, International journal of environmental research and public health.
[31] Lian Li,et al. Detection, mining and forecasting of impact load in power load forecasting , 2005, Appl. Math. Comput..
[32] V. Prybutok,et al. A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.
[33] Joaquín B. Ordieres Meré,et al. Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) , 2005, Environ. Model. Softw..
[34] 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.
[35] Yufang Wang,et al. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting , 2016 .
[36] Ching-Hsue Cheng,et al. A novel time-series model based on empirical mode decomposition for forecasting TAIEX , 2014 .
[37] Jian Wang,et al. A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction , 2017, Environmental research.
[38] Renhong Wang,et al. A Quasi-MQ EMD method for similarity analysis of DNA sequences , 2011, Appl. Math. Lett..
[39] Yuan Yin. Differences of Air Quality Index(AQI) and Air Pollution Index(API) , 2014 .
[40] P Hyde,et al. Forecasting PM10 in metropolitan areas: Efficacy of neural networks. , 2012, Environmental pollution.
[41] N Moussiopoulos,et al. Statistical analysis of environmental data as the basis of forecasting: an air quality application. , 2002, The Science of the total environment.
[42] J. Kukkonen,et al. Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki. , 2011, The Science of the total environment.
[43] Sancho Salcedo-Sanz,et al. Prediction of hourly O3 concentrations using support vector regression algorithms , 2010 .
[44] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[45] M. Kolehmainen,et al. Neural networks and periodic components used in air quality forecasting , 2001 .
[46] Norden E. Huang,et al. A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .
[47] Juan Manuel Górriz,et al. Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease , 2013, Expert Syst. Appl..
[48] Jianzhou Wang,et al. A seasonal hybrid procedure for electricity demand forecasting in China , 2011 .
[49] Masud Yunesian,et al. A novel, fuzzy-based air quality index (FAQI) for air quality assessment , 2011 .
[50] Ayse Betül Oktay,et al. Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks , 2010, Expert Syst. Appl..
[51] George E. P. Box,et al. Time Series Analysis: Forecasting and Control , 1977 .
[52] J. Mindell,et al. Predicted health impacts of urban air quality management , 2004, Journal of epidemiology and community health.
[53] P. Goyal,et al. Statistical models for the prediction of respirable suspended particulate matter in urban cities , 2006 .