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Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients

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Abstract

The Disturbance Storm Time (Dst) Index stands as a crucial geomagnetic metric, serving to quantify the intensity of geomagnetic disturbances. The accurate prediction of the Dst index plays a pivotal role in mitigating the detrimental effects caused by severe space-weather events. Therefore, Dst prediction has been a long-standing focal point within the realms of space physics and space-weather forecasting. In this study, a Temporal Convolutional Network (TCN) is deployed in tandem with the Integrated Gradient (IG) algorithm to predict the Dst index and scrutinize its associated physical processes. With these two components, our model can give the contribution of each input parameter to the outcome along with the forecast. The TCN component of our model utilizes interplanetary observational data, encompassing the vector magnetic field, solar-wind velocity, proton temperature, proton density, interplanetary electric field, and other relevant parameters for forecasting Dst indices. Despite the disparity in test sets, our model’s forecast accuracy approximates the error levels of the prior models. Remarkably, the prediction error of these machine-learning models has become comparable to the inherent error between the Dst index itself and the actual ring-current strength.

To understand the physical process behind the forecasting model, the IG algorithm was applied in our prediction model, in an attempt to analyze the underlying physical process of the machine-learning black box. In the temporal dimension, it is evident that the more recent the time, the more substantial the influence on the final prediction. Regarding the physical parameters, besides the historical Dst index itself, the flow pressure, the \(z\)-component of the magnetic field, and the proton density all significantly contribute to the final prediction. Additionally, IG attributions were analyzed for subsets of data, including different Dst-index ranges, different observation times, and different interplanetary structures. Most of the subsets exhibit an IG matrix with deviations from the mean distribution, which indicates a complex nonlinear system and sensitivity of the prediction to input values. These analyses align with physical reasoning and are in good agreement with previous research. The results affirm that the TCN+IG technique not only enhances space-weather forecast accuracy but also advances our comprehension of the underlying physical processes in space weather.

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Data Availability

No datasets were generated or analysed during the current study.

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Funding

This work is supported by grants from the NSFC (42325405, 42130204, 41904151, 42188101, 42074222) and the Strategic Priority Program of the Chinese Academy of Sciences (XDB41000000).

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Authors and Affiliations

  1. CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Sciences, University of Science and Technology of China, Hefei, China

    Junyan Liu, Zhihui Zhong, Dongwei Mao, Zhiyong Zhang & Can Wang

  2. Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China

    Chenglong Shen, Jiajia Liu & Yuming Wang

  3. CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China

    Chenglong Shen, Jiajia Liu & Yuming Wang

  4. School of Software Engineering, University of Science and Technology of China, Hefei, 230026, China

    Yang Wang

  5. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Yang Wang

  6. Deep Space Exploration Laboratory, Institute of Deep Space Sciences, Hefei, 230026, China

    Mengjiao Xu & Yutian Chi

  7. Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Mengcheng, Anhui, China

    Yuming Wang

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  6. Zhihui Zhong
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  7. Dongwei Mao
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  8. Zhiyong Zhang
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  9. Can Wang
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  10. Jiajia Liu
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  11. Yuming Wang
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Contributions

Junyan Liu and Chenglong Shen wrote the main manuscript. Yang Wang, Mengjiao Xu, Yutian Chi, Zhihui Zhong, Dongwei Mao, Can Wang, Jiajia Liu, and Yuming Wang contributed to the discussions and offered revision suggestions.

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Correspondence to Junyan Liu or Chenglong Shen.

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Liu, J., Shen, C., Wang, Y. et al. Forecasting the Dst Index with Temporal Convolutional Network and Integrated Gradients. Sol Phys 299, 98 (2024). https://doi.org/10.1007/s11207-024-02340-9

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