基于分布式感知深度神经网络的高分辨率PM2.5值估算
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刘基伟, 闵素芹, 金梦迪
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High resolution PM2.5 estimation based on the distributed perception deep neural network model
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LIU Jiwei, MIN Suqin, JIN Mengdi
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表4 2018年京津冀鲁地区PM2.5预测的误差和时间开销
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Tab. 4 Error and time cost of PM2.5 concentration prediction based on the Beijing-Tianjin-Hebei-Shandong region in 2018
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| DP-DNN | MLP | MI-NN | LSTM | GWR | B-OLSR | EN | | 地域迁移检验 | | MAE | 16.6 | 17.2 | 18.5 | 22.1 | 29.1 | 19.2 | 21.3 | | MAE_std | 1.1 | 1.2 | 1.6 | 0.9 | 41.0 | 0.4 | 0.6 | | RE(%) | 41.80 | 44.7 | 44.6 | 76.1 | 141.1 | 56.6 | 67.0 | | RE_std(%) | 4.90 | 4.72 | 5.0 | 13.7 | 141.7 | 4.3 | 5.7 | | MSE | 691.5 | 744.2 | 901.2 | 1026.6 | 63086.4 | 788.6 | 1008.9 | | MSE_std | 100.4 | 103.7 | 181.1 | 176.1 | 275336.9 | 49.0 | 81.0 | | RMSE | 26.6 | 27.2 | 29.9 | 31.9 | 100.3 | 28.1 | 31.7 | | RMSE_std | 1.9 | 1.9 | 2.9 | 2.6 | 230.3 | 0.9 | 1.3 | | 时间预测检验 | | MAE | 17.7 | 18.9 | 22.4 | 25.7 | 33.7 | 53.8 | 21.9 | | MAE_std | 3.7 | 5.0 | 7.4 | 4.7 | 2.7 | 12.0 | 4.3 | | RE(%) | 46.8 | 50.9 | 61.9 | 87.4 | 108.3 | 211.2 | 70.7 | | RE_std(%) | 7.2 | 11.7 | 20.8 | 18.8 | 26.9 | 71.3 | 10.9 | | MSE | 766.2 | 837.4 | 1126.8 | 1645.7 | 2228.3 | 3966.7 | 1038 | | MSE_std | 355.6 | 446.7 | 789.5 | 712.1 | 532.6 | 1300 | 489.7 | | RMSE | 26.9 | 27.4 | 31.9 | 39.6 | 46.9 | 62.1 | 31.3 | | RMSE_std | 6.1 | 5.0 | 10.3 | 8.9 | 5.1 | 10.5 | 7.6 | | 平均耗时(s) | 4.2 | 3.1 | 3.3 | 26.4 | 3389.0 | 1.3 | 0.5 |
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