| 摘要: |
| 针对数值模式和统计学习方法在海表面温度（SST）建模中的不足，将长短时记忆循环神经网络（LSTM-RNN）应用于SST的建模。使用研究海区24 a月平均的SST和太阳辐射、风场、蒸发降水等物理参数，通过LSTM-RNN构建西太平洋研究海区SST时间序列变化模型，用于预报研究海区下个月SST。建立了两个模型model1和model2，model1仅使用SST数据作为model2的对照，model2使用SST和其他物理参数。结果表明：model2在验证数据中的MAE为0.15℃，RMSE为0.19℃，相关性系数为0.978，和model1相比总体准确性提升31%，表明LSTM-RNN应用于SST建模是可行的；LSTM-RNN可以建立其他物理参数与SST的关系，从而显著提升海水表面温度模型的准确性。 |
| 关键词: 海洋物理学 LSTM-RNN SST 神经网络 |
| DOI：10.3969/J.ISSN.2095-4972.2019.02.005 |
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| 基金项目: |
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| Study on sea surface temperature model based on LSTM-RNN |
| ZHU Gui-chong,HU Song |
| (College of Marine Science, Shanghai Ocean University, Shanghai 201306, China) |
| Abstract: |
| Due to the shortcomings of numerical modeling and statistical learning methods in SST modeling, this study applies LSTM-RNN (long short term memory recurrent neural network) to improve the SST modeling. Using SST, solar radiation, wind field, evaporation，precipitation and other physical parameters of monthly averaged data of 24 years, the SST time series model of the Western Pacific is constructed by LSTM-RNN to predict the coming month's SST in the study area. Two models, model1 and model2, are established. Model1 only uses SST data as a comparison of model2 that consists of SST and physical parameters. The results show that the MAE of model2 in the valid set is 0.15℃, RMSE is 0.19℃ and the correlation coefficient is 0.978. Compared with model1, the overall accuracy of model2 is higher than 31%. It shows that the application of LSTM-RNN to SST modeling is feasible and LSTM-RNN can get the relationship between physical parameters and SST. Thus, the accuracy of the surface temperature model of sea water can be improved significantly. |
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