王舒扬1, 姜金荣2, 迟学斌2, 唐晓3
王舒扬, 姜金荣, 迟学斌, 唐晓. 融合数值模式预报数据的深度学习PM2.5浓度预测模型[J]. 数值计算与计算机应用, 2022, 43(2): 142-153.
Wang Shuyang, Jiang Jinrong, Chi Xuebin, Tang Xiao. A DEEP LEARNING MODEL FOR FORECASTING PM2.5 COMBINED WITH NUMERICAL MODEL DATA[J]. Journal on Numerica Methods and Computer Applications, 2022, 43(2): 142-153.
Wang Shuyang1, Jiang Jinrong2, Chi Xuebin2, Tang Xiao3
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[1] 崔相辉,谢剑锋,张丰,丁琳,李增顺,郝震寰,刘勇,赵起超.基于深度学习的PM2.5预测模型建立[J].北京测绘, 2017,(06):22-27. [2] 曲悦,钱旭,宋洪庆,何杰,李剑辉,修昊.基于机器学习的北京市PM2.5浓度预测模型及模拟分析[J].工程科学学报, 2019, 41(03):401-407. [3] Huang C, Kuo P. A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities[J]. Sensors, 2018, 18(7), 2220. [4] Kong, L, Tang X, Zhu J, Wang Z, Li J, Wu H, Wu Q, Chen H, Zhu L, Wang W, Liu B, Wang Q, Chen D, Pan Y, Song T, Li F, Zheng H, Jia G, Lu M, Wu L and Carmichael G R. A Six-year long (2013-2018) High-resolution Air Quality Reanalysis Dataset over China base on the assimilation of surface observations from CNEMC, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-100, in review, 2020. [5] Jin X, Yang N, Wang X, et al. Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction[J]. Applied Sciences, 2019, 9(21). [6] Hochreiter S, Schmidhuber, J. Long Short-Term Memory. Neural Computation. 1997, 9:1735-1780. [7] Shi X, Chen Z, Wang H, et al. Convolutional LSTM Network:a machine learning approach for precipitation nowcasting[C]. Neural information processing systems, 2015, 802-810. [8] Shi X, Gao Z, Lausen L, et al. Deep Learning for Precipitation Nowcasting:A Benchmark and A New Model[C]. Neural information processing systems, 2017, 5617-5627. [9] Bengio S, Vinyals O, Jaitly N, et al. Scheduled sampling for sequence prediction with recurrent Neural networks[C]. Neural information processing systems, 2015, 1171-1179. [10] Wang Y, Long M, Wang J, et al. PredRNN:recurrent neural networks for predictive learning using spatiotemporal LSTMs[C]. Neural information processing systems, 2017:, 879-888. [11] 许柏宁,姜金荣,郝卉群,林鹏飞,何丹丹.一种基于区域海表面温度异常预测的ENSO预报深度学习模型[J].科研信息化技术与应用, 2017, 8(6):65-76. [12] 张伟,王自发,安俊岭,等.利用BP神经网络提高奥运会空气质量实时预报系统预报效果[J].气候与环境研究, 2009, 15(5):595-601 [13] 程念亮,李红霞,孟凡,柴发合,程兵芬.我国城市PM2.5数值预报简述[J].安徽农业科学, 2015, 43(07):243-246+271. [14] 戴李杰,张长江,马雷鸣.基于机器学习的PM2.5短期浓度动态预报模型[J].计算机应用, 2017, 37(11):3057-3063. [15] 潘锦秀,晏平仲,孙峰,李云婷,刘保献,王占山,董瑞.多元线性回归方法对北京地区PM2.5预报的改进应用[J].中国环境监测, 2019, 35(02):43-52. |
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