Geomagnetic kp index forecast

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Machine Learning and Deep Learning for Geomagnetic Kp Index Forecast

Recent advances in machine learning and deep learning have significantly improved the accuracy and reliability of geomagnetic Kp index forecasts. Models such as Long Short-Term Memory (LSTM) networks, neural networks, and transformer-based frameworks have been developed to leverage large datasets of solar wind, interplanetary magnetic field parameters, and historical Kp values for both short-term and multi-day predictions. These models consistently outperform traditional statistical methods, especially in predicting geomagnetic storms (Kp ≥ 5), and provide valuable uncertainty quantification for risk management and preparedness Tan2018Wang2023Wang2022+3 MORE.

Key Predictors and Feature Selection in Kp Index Forecasting

Across multiple studies, the most influential predictors for Kp index forecasting are the southward component of the interplanetary magnetic field (Bz), solar wind speed, and historical Kp index values. The southward Bz is often the primary contributor, especially for storm-level events, while the importance of historical Kp values increases with longer forecast horizons. Feature selection and similarity algorithms help identify the most relevant parameters, improving model performance and interpretability Wang2023Wang2022Abduallah2022+1 MORE.

Short-Term and Multi-Day Kp Index Forecast Models

Short-term (1–9 hour ahead) Kp index forecasts benefit from deep learning models like transformer-based frameworks (e.g., KpNet, GNet), which combine transformer encoder blocks with Bayesian inference to quantify both data and model uncertainty. These models show lower root mean square errors and higher correlation coefficients compared to traditional approaches, and they provide actionable uncertainty estimates for operational use Li2025Wang2015.

For multi-day (3–5 day) forecasts, neural networks and machine learning models such as XGBoost and multimodal transformers integrate diverse data sources, including satellite measurements, solar images, and Kp time series. These models achieve high F1-scores for both storm and non-storm periods, accurately capturing the onset, intensity, and duration of geomagnetic storms Wang2023Wang2022Solares2016.

Statistical and Autoregressive Approaches

Autoregressive (AR) and ARIMA models, which rely solely on historical Kp time series, offer quick and computationally efficient forecasts. While their accuracy is generally lower than models incorporating real-time satellite data, they remain useful for rapid, resource-light predictions, especially during geomagnetic disturbances González2014Abduallah2023.

Explainable AI and Model Interpretability

Explainable AI techniques, such as integrated gradients, are increasingly used to quantify the contribution of each predictor to the Kp index forecast. This transparency helps researchers and forecasters understand model decisions, tailor forecasts to specific storm events, and improve trust in AI-driven predictions .

Probability and Conditional Forecasting

Conditional statistical models use previous Kp values, interplanetary magnetic field, and solar wind parameters to estimate the probability of different geomagnetic activity levels. These models provide reliable probability forecasts, especially for transitions between calm and storm conditions, and are validated using metrics like the relative operating characteristic area .

Conclusion

The integration of machine learning, deep learning, and explainable AI has led to substantial improvements in the accuracy, reliability, and interpretability of geomagnetic Kp index forecasts. By leveraging key physical parameters and historical data, these models provide valuable tools for space weather prediction, operational preparedness, and risk mitigation.

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Most relevant research papers on this topic

Geomagnetic Index Kp Forecasting With LSTM

The proposed model with long short-term memory improves the Kp geomagnetic index forecasting accuracy and accurately predicts geomagnetic storms (Kp 5).

Highly Cited

2018·

74citations

·Yao Tan et al.

Space Weather·

DOI

Planetary Kp index forecast using autoregressive models

The autoregressive model provides the lowest computational cost with satisfactory results for predicting geomagnetic Kp index values, without using satellite data.

2014·

0citations

·A. González et al.

arXiv: Space Physics

A machine learning-based model for the next 3-day geomagnetic index (Kp) forecast

Our machine learning-based model effectively predicts the next 3-day geomagnetic index (Kp) using similarity parameters from previous and current geomagnetic conditions.

2023·

23citations

·Jingjing Wang et al.

DOI

Kp Index Prediction Based on Similarity Algorithm of Machine Learning

The proposed machine learning model effectively predicts geomagnetic Kp index using historical solar wind data, providing a clearer and more intuitive comparison of solar wind characteristics.

2022·

1citation

·Zisiyu Wang et al.

Chinese Journal of Space Science·

DOI

A Transformer-Based Framework for Geomagnetic Activity Prediction

KpNet, a novel deep learning method, accurately predicts the Kp index, aiding in preparedness and disaster risk management, outperforming similar methods.

2022·

3citations

·Yasser Abduallah et al.

DOI

The geomagnetic storm and Kp prediction using Wasserstein transformer

The Wasserstein transformer-based framework accurately predicts geomagnetic activity, accurately capturing both quiet and storm phases, with potential for improved real-time forecasting.

Preprint

2025·

0citations

·Beibei Li

ArXiv·

DOI

Modeling and prediction of global magnetic disturbance in near‐Earth space: A case study for Kp index using NARX models

The direct approach outperforms the recursive approach in modeling and forecasting Kp index for global geomagnetic disturbances, but both methods produce slightly biased predictions for low and high disturbances.

2016·

53citations

·José Roberto Ayala Solares et al.

Space Weather·

DOI

A transformer-based framework for predicting geomagnetic indices with uncertainty quantification

GNet, a deep learning framework, accurately predicts geomagnetic indices with Bayesian inference, outperforming similar methods and providing data and model uncertainty quantification.

2023·

1citation

·Yasser Abduallah et al.

Journal of Intelligent Information Systems·

DOI

Statistical analysis and verification of 3‐hourly geomagnetic activity probability predictions

The conditional models using the previous Kp index, peak value of BtV, average value of Bz, and BzV provide a reliable 3-hourly geomagnetic activity probability forecast with a relative operating characteristic area of 0.64.

2015·

17citations

·Jingjing Wang et al.

Space Weather·

DOI

Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index

Machine learning techniques can improve Kp index prediction accuracy by selecting the most important inputs and reducing input dimensionality.

2019·

29citations

·I. Zhelavskaya et al.

Space Weather·

DOI

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