Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification
Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification
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DOI：
10.1029/2023sw003824
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发表时间：
2024-02
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通讯作者：
Yasser Abduallah;Khalid A. Alobaid;Jason T. L. Wang;Haimin Wang;V. Jordanova;Vasyl Yurchyshyn;Huseyin Cavus;Ju Jing
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作者：
Yasser Abduallah;Khalid A. Alobaid;Jason T. L. Wang;Haimin Wang;V. Jordanova;Vasyl Yurchyshyn;Huseyin Cavus;Ju Jing
我们提出了一种名为SYMHnet的新型深度学习框架，该框架采用图神经网络和双向长短期记忆网络，从太阳风与行星际磁场参数中协同学习模式，基于1分钟和5分钟分辨率的数据对SYM - H指数进行短期预测。SYMHnet将美国国家航空航天局（NASA）空间科学数据协调档案库提供的参数值时间序列作为输入，并针对给定时间点t，预测时间点t + w小时的SYM - H指数值作为输出，其中w为1或2。通过将贝叶斯推理融入学习框架，SYMHnet在预测未来SYM - H指数时，能够量化随机（数据）不确定性和认知（模型）不确定性。实验结果表明，无论是1分钟还是5分钟分辨率的数据，SYMHnet在平静期和风暴期都表现良好。结果还显示，SYMHnet总体上比相关机器学习方法表现更优。例如，在使用5分钟分辨率数据预测一场大风暴（SYM - H = - 393 nT）中的SYM - H指数（提前1小时）时，SYMHnet的预测技巧得分（FSS）达到0.343，而近期一种梯度提升机（GBM）方法的FSS仅为0.074。在预测该大风暴中的SYM - H指数（提前2小时）时，SYMHnet的FSS达到0.553，而GBM方法的FSS为0.087。此外，SYMHnet能够提供数据和模型不确定性量化结果，而相关方法则无法做到。
We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.