E3S Web Conf.
Volume 269, 20212021 International Conference on Environmental Engineering, Agricultural Pollution and Hydraulical Studies (EEAPHS 2021)
|Number of page(s)||8|
|Published online||09 June 2021|
- Perrone, M.G., M. Gualtieri, V. Consonni, L. Ferrero, G. Sangiorgi, E. Longhin, D. Ballabio, E. Bolzacchini, M. Camatini, Particle size, chemical composition, seasons of the year and urban, rural or remote site origins as determinants of biological effects of particulate matter on pulmonary cells, Environ. Pollut., 176, 215--227 (2013). [Google Scholar]
- R. Bono, R. Tassinari, V. Bellisario, G. Gilli, M. Pazzi, V. Pirro, G. Mengozzi, M. Bugiani, P Piccioni, Urban air and tobacco smoke as conditions that increase the risk of oxidative stress and respiratory response in youth, Environmental Research, 137, 141–-146 (2015). [Google Scholar]
- H. Jianjun, G. Sunling, Y. Ye, Y. Lijuan, W. Lin, M. Honjun, S. Congbo, Z. Suping, L. Hongli, L. Xiaoyu, L. Ruipeng, Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities, Environ. Pollut., 223, 484–-496 (2017). [Google Scholar]
- C. Cheng, W. Hongjie, L. Weisheng, F. Qiming, T. Ye, Indoor PM2.5 Prediction Based on MultiInstance Genetic Neural Network (In Chinese), Computer Applications and Software, 36(5), 235--241 (2019). [Google Scholar]
- Z. Guowei, W. Tengjun, PM2.5 Concentration Prediction Model Based on SVM-wavelet Neural Network (In Chinese), Sichuan Environment, 37(6), 141-144 (2018). [Google Scholar]
- C. Qiang, M. Kun, Z. Huimin, C. Xianlei, Z. Minghua, Study on Spatiotemporal Variability of PM2. 5Concentrations and Prediction Model over Zhengzhou City (In Chinese), Environmental Monitoring in China, 31(3), 105-112 (2015). [Google Scholar]
- Y. Sun, Q. Zeng, B. Geng, X. Lin, B. Sude, L. Chen, Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing, IEEE Geoscience and Remote Sensing Letters, 16(9), 1343-1347 (2019). [Google Scholar]
- Z. Wenfang, L. Runsheng, T. Wei, Z. Yong, Forecasting Model of Short-Term PM2.5 Concentration Based on Deep Learning (In Chinese), Journal of Nanjing Normal University (Natural Science Edition), 3, 32-41 (2019). [Google Scholar]
- T. Li, M. Hua, X. Wu, A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5), IEEE ACCESS, (8), 26933-26940, (2020). [Google Scholar]
- L. Xulin, Z Wenfang, T Wei, Forecasting Model of PM2. 5 Concentration one Hour in Advance Based on CNN-Seq2Seq (In Chinese), Journal of Chinese Computer Systems, 41(05), 1000--1006 (2020). [Google Scholar]
- F. C., Gene Expression Programming: a new adaptive algorithm for solving problems, Complex System, 13(2), 87-129 (2001). [Google Scholar]
- L. Haifeng, L Minyan, Z Min, H Baiqiao, Application of Gene Expression Programming in Software Reliability Modeling (In Chinese), Journal of Frontiers of Computer Science and Technology, 5(6), 534-546 (2011). [Google Scholar]
- M. Saeid, B. Javad, K. Keivan, Application of gene expression programming to predict daily dew point temperature, Applied Thermal Engineering, 112, 1097-1107 (2017). [Google Scholar]
- S. Hr. Aghay Kabolia, A. Fallahpoura, J. Selvaraja, N.A. Rahim a b, Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming, Energy, 126, 144-164 (2017). [Google Scholar]
- I. Muhammad Farjad, L. Qingfeng, A. Iftikhar, Z. Xingyi, Y. Jian, J. Muhammad Faisal, R. Momina, Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming, Journal of Hazardous Materials, 384, 121322 (2020). [Google Scholar]
- L. Debin, X. Jianhua, Y. Wenze, M. Wanliu, Yang Dongyang, W. Jinzhu, Response of PM 2.5 pollution to land use in China, Journal of Cleaner Production, 2020, 244, 118741-118741 (2020). [Google Scholar]
- W. Zhuang, Z. Shuo, Study on the spatial– temporal change characteristics and influence factors of fog and haze pollution based on GAM, Neural Computing and Application, 31(05), 1619-1631 (2019). [Google Scholar]
- B. Lv, W. G. Cobourn, Y. Bai, Development of nonlinear empirical models to forecast daily PM 2.5 and ozone levels in three large Chinese cities, Atmospheric Environment, 147, 209-223 (2016). [Google Scholar]
- W. Yuan, K. Wang, X. Bo, L. Tang, J Wu, A novel multi-factor & multi-scale method for PM2.5 concentration forecasting, Environmental Pollution, 255(1), 113187 (2019). [Google Scholar]
- L. Suixin, C. Junji, A. Zhisheng. Characterization of Ambient Fine Particles (PM2.5) Concentration and Its Influential Factors (In Chinese), The Chinese Journal of Process Engineering, 9(S2), (2009) [Google Scholar]
- M. Zhaowei, L. Peiyu, Z. tongjun, C. Changfeng, Characteristics and Meteorological Influencing Factors of PM2.5 Mass Concentration in Two Urban Districts of Xi’an During 2015-2018 (In Chinese), Journal of Hygiene Research, 49(01), 75-79 (2020). [Google Scholar]
- P. Yan, Z. Ziru, W. Tingxian, W. jie, Prediction of PM2.5 Concentration Based on Ensemble Learning (In Chinese), Journal of Beijing University of Posts and Telecommunications, doi: 10.13190/j.jbupt. 2019-153. [Google Scholar]
- Q. Chao, C. Tingting, L. Jia, L. Yudong. SpatioTemporal Characteristics of PM (2.5) and Influence Factors in Typical Cities of China (In Chinese). Research of Environmental Sciences, 32(07), 1117-1125 (2019). [Google Scholar]
- S. Aydin, S. Mohsen, K. Anikender, G. Hossein, Prediction of air quality in Tehran by developing the nonlinear ensemble model, Journal of Cleaner Production, 259, 120825, (2020). [Google Scholar]
- Y. Changan, T. Changjie, Z. Jie, Function Mining Based on Gene Expression Programming Convergency Analysis and Remnant-guided Evolution Algorithm (In Chinese), Advanced Engineering Sciences, 36(6), 100-105 (2004). [Google Scholar]
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