Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (6): 1528-1537.doi: 10.23940/ijpe.19.06.p4.15281537
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Wenfang Zhaoa,b, Yong Zhouc,*, and Wei Tangc
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Wenfang Zhao is a senior engineer at the Beijing Meteorological Information Center, Beijing Meteorological Bureau, Beijing, China. Her research interests include big data, deep learning, machine learning, and big data analysis;Yong Zhou is a senior engineer at the Development and Research Center, China Meteorological Administration, Beijing, China. His main research interests include atmospheric sounding, instrument development, and atmospheric environment;Wei Tang is a senior engineer at the Development and Research Center, China Meteorological Administration, Beijing, China. Her current research interests include data mining and machine learning.
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Wenfang Zhao, Yong Zhou, and Wei Tang. Novel Convolution and LSTM Model for Forecasting PM2.5 Concentration [J]. Int J Performability Eng, 2019, 15(6): 1528-1537.
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[1] Y. J. Kaufman, T. Didier,B. Olivier, “A Satellite View of Aerosols in the Climate System,” [2] C. K.Chan and X. H. Yao, “Air Pollution in Mega Cities in China,” [3] X. Y.Zhang and H. B. Hu, “Risk Assessment of Exposure to PM2.5 in Beijing using Multi-Source Data,” [4] Q. Jin, X. Fang, B. Wen,A. Shan, “Spatiotemporal Variations of PM2.5 Emission in China from2005 to 2014,” [5] A. V. Donkelaar, R. V. Martin, M. Brauer,B. L. Boys, “Use of Satellite Observations for Long-Term Exposure Assessment of Local Concentrations of Fine Particulate,” [6] C. L. Fang, Z. B. Wang,G. Xu, “Spatial-Temporal Characteristics of PM2.5 in China: A City Level Perspective Analysis,” [7] X. Su, W. Gough,Q. Shen, “Correlation of PM2.5 and Meteorological Variables in Ontario Cities: Statistical Downscaling Method Coupled with Artificial Neural Network,” in [8] R. Chen, X. Wang, X. Meng, J. Hua, Z. J. Zhou, B. H. Chen, et al., “Communicating Air Pollution-Related Health Risks to the Public: An Application of the Air Quality Health Index in Shanghai, China,” [9] J. Chen, J. Lu, J. C. Avise, J. A.DaMass, M. J. Kleeman, and A. P. Kaduwela, “Seasonal Modeling of PM2.5 in California's San Joaquin Valley,” [10] Q. Z. Wu, W. S. Xu, A. Shi, Y. Li, X. J. Zhao, Z. F. Wang, et al., “Air Quality Forecast of PMl0 in Beijing with Community Multi-Scale Air Quality Modeling (CMAQ) System: Emission and Improvement,” [11] L. Chen, D. M. Wu,Q. Chen, “Prediction of Air Pollution based on Wavelet Analysis and Support Vector Machine,” [12] G. -Q. Zhou, Y. Xie, J. -B. Wu, Z. -Q. Yu, L. -Y. Chang, and W. Gao, “WRF-Chem based PM2.5 Forecast and Bias Analysis over the East China Region,” [13] G. Yi and M. G. Zhang, “Numerical Simulation of a Heavy Fog-Haze Episode over the North China Plain in January2013,” [14] H. Z. De, H. X. Yun,H. X. Yong, “Haze Forecast based on Time Series Analysis and Kalman Filtering,” [15] J. Z. Xiu, X. Jing,Z. Z. Yin, “Beijing Regional Environmental Meteorology Prediction System and its Performance Test of PM2.5 Concentration,” [16] T. J. Wang, F. Jiang, J. J. Deng, Y. Shen, Q. Y. Fu, Q. Wang, et al., “Urban Air Quality and Regional Haze Weather Forecast for Yangtze River Delta,” [17] J. Liu, P. Yang, W. S. Lv, A. Liu,J. X. Liu, “Prediction Model of PM2. 5 Mass Concentrations based on Fuzzy Time Series and Support Vector Machine,” [18] L. Li, L. Ma, J. F. He, D. G. Shao, S. L. Yi, Y. Xiang, et al., “PM2.5 Concentration Prediction Model of Least Squares Support Vector Machine based on Feature Vector,” [19] J. Pan, H. Z. Wang, H. Gao, W. X. Zhao, H. X. Huo,H. R. Dong, “Paradise Pointer: A Sightseeing Scenes Images Search Engine based on Big Data Processing,” in [20] D. Mishra, P. Goyal,A. Upsfhysy, “Artificial Intelligence based Approach to Forecast PM2.5 During Haze Episodes: A Case Study of Delhi, India,” [21] G. O. Philip, L. Gunnar,D. Ottfried, “Relationship Between Rice Yield and Climate Variables in Southwest Nigeria using Multiple Linear Regression and Support Vector Machine Analysis,” [22] X. J. Shi, Z. R. Chen, H. Wang, D. Y. Yeung, W. K. Wong,W. C. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in [23] J. C. Zhao, F. Deng, Y. Y. Cai,J. Chen, “Long Short-Term Memory-Fully Connected (LSTM-FC) Neural Network for PM2.5 Concentration Prediction,” [24] C. Vidushi, D. Anand, K. Vijayan, et al., “Time Series based LSTM Model to Predict Air Pollutant's Concentration for Prominent Cities in India,” in [25] S. A. Weber, T. Z. Insaf, E. S. Hall, T. O. Talbot,A. K. Huff, “Assessing the Impact of Fine Particulate Matter (PM2.5) on Respiratory-Ardiovascular Chronic Diseases in the New York City Metropolitan Area using Hierarchical Bayesian Model Estimates,” [26] J. H.Chou and H. K. Ping, “A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities,” [27] C. C. Wen, S. F. Liu, X. J. Yao, L. Peng, X. Li, Y. Hu, et al., “A Novel Spatiotemporal Convolutional Long Short-Term Neural Network for Air Pollution Prediction,” [28] W. S. Ping, W. C. Jia,W. H. Jin, “Adaptive Deep Learning-based Air Quality Prediction Model using the Most Relevant Spatial-Temporal Relations,” [29] D. Chu, Y. J. Kaufman, G. Zibordi, J. Chern, J. T. Mao, C. C. Li, et al., “Global Monitoring of Air Pollution Over Land from the Earth Observing System-Terra Moderate Resolution Imaging Spectro-Radiometer (MODIS),” [30] P. E. Saide, G. R. Carmichael, S. N. Spak, L. Gallardo, A. E. Osses, M. A.Mena-Carrasco, et al., “Forecasting Urban PM10 and PM2.5 Pollution Episodes in Very Stable Nocturnal Conditions and Complex Terrain using WRF-Chem CO Tracer Model,” [31] T. Du, L. Bourdev, R. Fergus, L. Torresani,M. Paluri, “Learning Spatiotemporal Features with 3D Convolutional Networks,” in [32] Y. Chen, R. Shi, S. Shu,W. Gao, “Ensemble and Enhanced PM10 Concentration Forecast Model based on Stepwise Regression and Wavelet Analysis,” [33] Z. Liang, G. Z. Guan,Y. S. Pei, “Learning Spatiotemporal Features using 3DCNN and Convolutional LSTM for Gesture Recognition,” in |
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