张雪薇, 韩震. Argo温度数据的ConvGRU模型预测分析[J]. 海洋环境科学, 2022, 41(4): 628-635. DOI: 10.12111/j.mes.20210054
引用本文: 张雪薇, 韩震. Argo温度数据的ConvGRU模型预测分析[J]. 海洋环境科学, 2022, 41(4): 628-635. DOI: 10.12111/j.mes.20210054
ZHANG Xue-wei, HAN Zhen. Prediction and analysis of Argo temperature data by ConvGRU model[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2022, 41(4): 628-635. DOI: 10.12111/j.mes.20210054
Citation: ZHANG Xue-wei, HAN Zhen. Prediction and analysis of Argo temperature data by ConvGRU model[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2022, 41(4): 628-635. DOI: 10.12111/j.mes.20210054

Argo温度数据的ConvGRU模型预测分析

Prediction and analysis of Argo temperature data by ConvGRU model

  • 摘要: Argo(Array for Real-time Geostrophic Oceanography)是海洋环境信息的重要来源之一,可通过自动剖面浮标、卫星定位和数据同化等技术获取大范围全球海洋上层之间的海温剖面资料。本文利用ConvGRU(Convolutional Gate Recurrent Unit)作为Argo温度的预测模型,以西北太平洋部分海域为研究区域,选取2004-2018年Argo数据作为训练数据,对2019年的0 m、50 m、100 m、200 m和300 m 不同深度位置水平剖面进行了预测分析。研究结果表明:ConvGRU模型对Argo温度数据的变化趋势具有较好的模拟能力;预测模型的训练集和验证集的RMSE(root mean squared error)均值分别为0.0462 ℃和0.0463 ℃,MAE(mean absolute error)均值分别为0.0442 ℃和0.0450 ℃,其Acc(accuracy)都在99%以上;对于预测评估,RE(relative error)的误差范围为0.0228~0.0427,预测变化的空间特性与真实值的吻合程度较高。

     

    Abstract: Argo (Array for Real-time Geostrophic Oceanography) is one of the main sources of marine environmental information, which can obtain a large range of global upper ocean surface temperature profile data, through automatic profiling buoy, satellite positioning, data assimilation and other technologies. In this paper, ConvGRU (Convolutional Gate Recurring Unit) was used as the prediction model of Argo temperature, and part of the Northwest Pacific Ocean was taken as the study area, and Argo data from 2004 to 2018 were selected as the training data. The horizontal sections at the depth of 0 m, 50 m, 100 m, 200 m and 300 m in 2019 are predicted and analyzed. The results show that the ConvGRU model has a good ability to simulate the variation trend of Argo temperature data. The RMSE (Root Mean Squared Error) of the training set and the verification set of the prediction model were 0.0462 ℃ and 0.0463 ℃ respectively, and the MAE(Mean Absolute Error) were 0.0442 ℃ and 0.0450 ℃ respectively. Its Acc (Accuracy) is above 99%; For the prediction assessment, the error range of RE (Relative Error) is between 0.0228-0.0427, indicating that the spatial characteristics of the predicted variation are in good agreement with the real value.

     

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