基于LSTM-RNN的海水表面温度模型研究

Study on sea surface temperature model based on LSTM-RNN

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。

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.