TLDR: This research paper explores LSTM-based sequence-to-sequence deep learning models to predict 24-hour solar proton flux profiles after Solar Proton Event (SPE) onsets. Using a dataset of 40 well-connected SPEs from 1997-2017, the study compares various forecasting strategies: proton-only vs. proton+X-ray input, original vs. trend-smoothed data, and autoregressive vs. one-shot prediction. Key findings indicate that one-shot forecasting generally yields lower error, proton-only models perform better on original data, trend-smoothing significantly enhances proton+X-ray models, and architectural choices can sometimes outweigh data preprocessing benefits, with the best model trained on original data.
Solar Proton Events (SPEs) pose significant radiation risks to satellites, astronauts, and various technological systems. Predicting the precise timing and intensity of these events is crucial for issuing early warnings and implementing protective measures. A recent study delves into the application of deep learning, specifically Long Short-Term Memory (LSTM) networks configured as sequence-to-sequence (seq2seq) models, to forecast 24-hour proton flux profiles following SPE onsets.
The research, titled “Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Profiles Using GOES Data,” was conducted by Kangwoo Yi, Bo Shen, Qin Li, Haimin Wang, Yong-Jae Moon, Jaewon Lee, and Hwanhee Lee. Their work addresses the inherent challenges in forecasting SPEs, such as their infrequent occurrence, the non-repetitive and impulsive nature of their flux profiles, and the complex variability of multi-input solar eruption parameters.
To tackle these issues, the team utilized a carefully curated dataset of 40 well-connected SPEs observed by NOAA GOES satellites between 1997 and 2017. Each event was linked to a significant M-class solar flare in the western hemisphere and featured undisturbed proton flux profiles. The study employed a 4-fold stratified cross-validation approach to ensure robust performance evaluation, given the limited number of historical events.
The core of the methodology involved comparing various seq2seq model configurations. These models, each comprising an encoder and a decoder with two stacked LSTM layers, were tested under different forecasting scenarios. The researchers systematically varied architectural hyperparameters, including the number of LSTM units (hidden dimensionality) and the embedding size, creating 15 distinct model architectures.
Exploring Forecasting Strategies
The study evaluated six distinct forecasting strategies, combining different input data, preprocessing methods, and prediction modes:
- Input Features: Models were tested with either proton flux data alone (mono-feature) or a combination of proton and X-ray flux data (multi-input). The goal was to see if adding real-time flare data improved proton flux forecasts.
- Preprocessing Method: The models were trained on both original flux data, which includes all short-term variability, and a trend-smoothed version, which emphasizes the 1-hour trend by filtering out high-frequency fluctuations.
- Forecasting Mode: Two modes were compared: autoregressive, where the decoder predicts step-by-step with each output fed back as input for the next step, and one-shot, where the decoder generates the entire 24-hour sequence at once.
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Key Findings and Insights
The experiments yielded several significant insights into effective SPE forecasting:
First, one-shot forecasting consistently outperformed the autoregressive approach. This is likely because autoregressive models accumulate errors at each step, whereas one-shot models benefit from a global context and generate the full output sequence in a single pass, avoiding error propagation.
Second, when using original (non-detrended) data, proton-only models generally performed better than models that included X-ray flux data. The raw X-ray signal, with its sharp, high-frequency components, tended to introduce noise that could mislead the model. However, this trend shifted when trend-smoothed data was used.
Third, trend-smoothing significantly improved the performance of proton+X-ray models. By averaging input over a sliding window, smoothing reduced high-frequency fluctuations in the X-ray flux, providing a more stable representation of the flare’s energy release. This enhanced the compatibility between the heterogeneous inputs in multi-modal forecasting tasks.
Finally, despite the overall advantage of trend-smoothed data for multi-input models, the best-performing model configuration was actually trained on original data. This suggests that specific architectural choices and optimization strategies can sometimes be more impactful than data preprocessing alone.
The research highlights the feasibility of using deep learning for near-real-time solar proton flux profile prediction, offering valuable insights for designing operational space weather forecasting systems. The findings underscore the importance of selecting appropriate forecasting strategies and model architectures, especially when dealing with limited, domain-specific datasets. For more detailed information, you can read the full research paper here.
