PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy

Sustainable Cities and Society - Tập 85 - Trang 104049 - 2022
Yang Yu1, Hongtao Li1, Shaolong Sun, Yongwu Li2
1School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
2College of Economics and Management, Beijing University of Technology, Beijing 100124, China

Tài liệu tham khảo

Ali, 2019, Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms, Journal of Hydrology, 576, 164, 10.1016/j.jhydrol.2019.06.032 Almani, 2020, Fractional Brownian motion with two-variable hurst exponent, Journal of Computational and Applied Mathematics, 388 Cheng, 2019, Lidar data assimilation method based on CRTM and WRF-chem models and its application in PM2.5 forecasts in Beijing, Science of the Total Environment, 682, 541, 10.1016/j.scitotenv.2019.05.186 Cheng, 2021, Pathways of China’s PM2.5 air quality 2015–2060 in the context of carbon neutrality, National Science Review, 8, 10.1093/nsr/nwab078 Cheng, 2019, Hybrid algorithm for short-term forecasting of PM2.5 in China, Atmospheric Enviroment, 200, 264, 10.1016/j.atmosenv.2018.12.025 De Marco, 2019, Impacts of air pollution on human and ecosystem health, and implications for the national emission ceilings directive: Insights from Italy, Environment International, 125, 320, 10.1016/j.envint.2019.01.064 Diebold, 1995, Comparing predictive accuracy, Journal of Business & Economic Statistics, 13, 134 Dong, 2021, Air pollution forecasting with multivariate interval decomposition ensemble approach, Atmospheric Pollution Research, 10.1016/j.apr.2021.101230 Du, 2020, A novel hybrid model based on multi-objective harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting, Applied Soft Computing, 96, 10.1016/j.asoc.2020.106620 Franceschi, 2018, Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogota, Colombia, using artificial neural networks, principal component analysis, and k-means clustering, Atmospheric Pollution Research, 9, 912, 10.1016/j.apr.2018.02.006 Gao, 2021, A graph-based LSTM model for PM2.5 forecasting, Atmospheric Pollution Research, 12, 10.1016/j.apr.2021.101150 Garcin, 2017, Estimation of time-dependent hurst exponents with variational smoothing and application to forecasting foreign exchange rates, Physica A: Statistical Mechanics and its Applications, 483, 462, 10.1016/j.physa.2017.04.122 Halkos, 2019, Understanding transboundary air pollution network: Emissions, depositions and spatio-temporal distribution of pollution in European region, Resources, Conservation and Recycling, 145, 113, 10.1016/j.resconrec.2019.02.014 Huang, 2022, Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting, Energy, 239, 10.1016/j.energy.2021.122245 Huang, 2021, PM2.5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition, Science of the Total Environment, 768, 10.1016/j.scitotenv.2020.144516 Huang, 2011, Extreme learning machines: A survey, International Journal of Machine Learning and Cybernetics, 2, 107, 10.1007/s13042-011-0019-y Jin, 2020, Forecasting air passenger demand with a new hybrid ensemble approach, Journal of Air Transport Management, 83, 10.1016/j.jairtraman.2019.101744 Jin, 2022, A novel multi-modal analysis model with baidu search index for subway passenger flow forecasting, Engineering Applications of Artificial Intelligence, 107, 10.1016/j.engappai.2021.104518 Ke, 2022, Development and application of an automated air quality forecasting system based on machine learning, Science of the Total Environment, 806, 10.1016/j.scitotenv.2021.151204 Li, 2020, Concentration estimation of dissolved oxygen in pearl river basin using input variable selection and machine learning techniques, Science of the Total Environment, 731, 10.1016/j.scitotenv.2020.139099 Li, 2022, Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis, Environmental Modelling & Software, 149, 10.1016/j.envsoft.2022.105329 Li, 2020, The forecasting of passenger demand under hybrid ridesharing service modes: A combined model based on WT-FCBF-LSTM, Sustainable Cities and Society, 62, 10.1016/j.scs.2020.102419 Liu, 2020, A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2.5 concentrations, Information Sciences, 516, 266, 10.1016/j.ins.2019.12.054 Liu, 2021, Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting, Energy Conversion and Management, 238, 10.1016/j.enconman.2021.114136 Liu, 2019, Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China, Atmospheric Pollution Research, 10, 1588, 10.1016/j.apr.2019.05.007 Liu, 2021, A spatial multi-resolution multi-objective data-driven ensemble model for multi-step air quality index forecasting based on real-time decomposition, Computers in Industry, 125, 10.1016/j.compind.2020.103387 Lu, 2021, Prediction into the future: A novel intelligent approach for PM2.5 forecasting in the ambient air of open-pit mining, Atmospheric Pollution Research, 12, 10.1016/j.apr.2021.101084 Menares, 2021, Forecasting PM2.5 levels in santiago de Chile using deep learning neural networks, Urban Climate, 38, 10.1016/j.uclim.2021.100906 Mi, 2019, Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine, Energy Conversion and Management, 180, 196, 10.1016/j.enconman.2018.11.006 Niu, 2019, A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting, Applied Energy, 241, 519, 10.1016/j.apenergy.2019.03.097 Ofori-Ntow Jnr, 2021, Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting, Sustainable Cities and Society, 66, 10.1016/j.scs.2020.102679 Pedregosa, 2011, Scikit-learn: Machine learning in python, Journal of Machine Learning Research, 12, 2825 Rehman, 2010, Multivariate empirical mode decomposition, Proceedings of the Royal Society of London, Series A (Mathematical and Physical Sciences), 466, 1291 Rezaei, 2020, Large-scale climate variability controls on climate, vegetation coverage, lake and groundwater storage in the lake urmia watershed using SSA and wavelet analysis, Science of the Total Environment, 724, 10.1016/j.scitotenv.2020.138273 Samal, 2021, Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach, Urban Climate, 36, 10.1016/j.uclim.2021.100800 Samal, 2021, An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach, Sustainable Cities and Society, 70, 10.1016/j.scs.2021.102923 Shindell, 2019, Climate and air-quality benefits of a realistic phase-out of fossil fuels, Nature, 573, 408, 10.1038/s41586-019-1554-z Sun, 2021, Improvement of PM2.5 and O3 forecasting by integration of 3D numerical simulation with deep learning techniques, Sustainable Cities and Society, 75, 10.1016/j.scs.2021.103372 Sun, 2020, Hourly PM2.5 concentration forecasting based on feature extraction and stacking-driven ensemble model for the winter of the Beijing-Tianjin-Hebei area, Atmospheric Pollution Research, 11, 110, 10.1016/j.apr.2020.02.022 Uno, 2020, Paradigm shift in aerosol chemical composition over regions downwind of China, Scientific Reports, 10, 6450, 10.1038/s41598-020-63592-6 Wang, 2022, A combined forecasting system based on multi-objective optimization and feature extraction strategy for hourly PM2.5 concentration, Applied Soft Computing, 114, 10.1016/j.asoc.2021.108034 Wang, 2021, Influence of pollution reduction interventions on atmospheric PM2.5: A case study from the 2017 Xiamen, Atmospheric Pollution Research, 12, 10.1016/j.apr.2021.101137 Wang, 2019, A hybrid-wavelet model applied for forecasting PM2.5 concentrations in Taiyuan city, China, Atmospheric Pollution Research, 10, 1884, 10.1016/j.apr.2019.08.002 Wetschoreck, 2020 Wu, 2019, Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network, Sustainable Cities and Society, 50, 10.1016/j.scs.2019.101657 Wu, 2020, PM2.5 concentrations forecasting using a new multi-objective feature selection and ensemble framework, Atmospheric Pollution Research, 11, 1187, 10.1016/j.apr.2020.04.013 Yang, 2021, Global burden of COPD attributable to ambient PM2.5 in 204 countries and territories, 1990 to 2019: A systematic analysis for the global burden of disease study 2019, Science of the Total Environment, 796, 10.1016/j.scitotenv.2021.148819 Zhai, 2019, Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmospheric Chemistry and Physics, 19, 11031, 10.5194/acp-19-11031-2019 Zhang, 2003, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 159, 10.1016/S0925-2312(01)00702-0 Zhang, 2021, Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism, Renewable Energy, 174, 688, 10.1016/j.renene.2021.04.091 Zhao, 2021, A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and vs measurements, Soil Dynamics and Earthquake Engineering, 150, 10.1016/j.soildyn.2021.106930 Zhou, 2021, Predictions and mitigation strategies of PM2.5 concentration in the yangtze River Delta of China based on a novel nonlinear seasonal grey model, Environmental Pollution, 276, 10.1016/j.envpol.2021.116614