HOURLY TEMPERATURE FORECAST METHOD OF SINGLE STATION BASED ON 1DCNN AND LSTM
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摘要: 针对海量气象观测数据间存在大量的物理噪声、与气温无关的冗余特征以及时间相关性，提出了一种将一维卷积神经网络(1DCNN)和长短期记忆神经网络(LSTM)相结合的多信息融合气温预报方法。首先，运用差分法对气象观测数据进行预处理，得到平稳时间序列数据；其次，运用1DCNN提取与气温变化相关的特征变量作为神经网络模型的输入变量；最后，运用1DCNN和LSTM构建多信息融合气温预报模型1DCNN-LSTM，并以云南省昆明市历史气象观测数据为例，与传统的LSTM、1DCNN和反向传播神经网络(BP)对未来24小时的逐时气温预报进行了比较研究。研究结果表明，1DCNN-LSTM的均方根误差(RMSE)相较于LSTM、1DCNN和BP最大降低了5.221%、19.350% 和9.253%，平均绝对误差(MAE)最大降低了4.419%、17.520% 和8.089%。为气温的精准预报提供了参考依据。Abstract: To deal with the large amount of physical noise, redundant features unrelated to temperature, and time correlation between massive meteorological observational data, a multi-information fusion temperature forecast method combining one-dimensional convolutional neural network (1DCNN) and long short-term memory neural network (LSTM) is proposed. First, the difference method is used to preprocess the meteorological observational data to obtain stationary time series data. Second, 1DCNN is used to extract feature variables related to temperature changes as the input variables of the neural network model. Finally, 1DCNN and LSTM are used to establish a multi-information fusion temperature prediction model 1DCNN-LSTM. Taking the historical meteorological observational data of Kunming City in Yunnan Province as an example, the model is compared with the traditional LSTM, 1DCNN and Back Propagation Neural Network (BP) of the hourly temperature forecast in the next 24 hours. The results show that compared with those of LSTM, 1DCNN and BP, the root mean square error (RMSE) of 1DCNN-LSTM is reduced by 5.221%, 19.350%, and 9.253%, and the mean absolute error (MAE) is reduced by 4.419%, 17.520% and 8.089%, respectively. This research method provides a reference for the accurate prediction of air temperature.
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表 1 1DCNN-LSTM相较于1DCNN、LSTM和BP模型在RMSE、MAE、r和p上改进的百分比
模型 评价指标 1小时 2小时 3小时 4小时 5小时 6小时 7小时 8小时 9小时 10小时 LSTM RMSE 5.221 4.216 3.651 3.184 2.493 2.103 1.703 1.219 0.854 0.519 MAE 4.419 3.757 3.415 3.095 2.509 2.129 1.760 1.370 1.141 0.676 r 0.119 0.121 0.123 0.125 0.126 0.128 0.129 0.130 0.130 0.131 p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1DCNN RMSE 19.350 16.241 14.335 12.497 10.650 9.331 7.973 6.634 5.552 4.694 MAE 17.520 15.140 13.541 12.060 10.497 8.967 7.878 6.548 5.317 4.384 r 0.435 0.443 0.451 0.458 0.465 0.470 0.475 0.478 0.480 0.482 p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 BP RMSE 9.253 7.685 6.798 5.908 4.955 4.264 3.545 2.753 2.110 1.527 MAE 8.089 7.034 6.332 5.757 4.951 4.289 3.650 2.917 2.358 1.713 r 0.225 0.228 0.232 0.235 0.238 0.240 0.242 0.244 0.245 0.246 p 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 
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