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<p>The ionospheric D-region (60–90&#xa0;km) critically controls very low frequency (VLF) radio wave propagation and responds rapidly to solar flare radiation. Accurate prediction of D-region electron density is therefore essential for reliable space weather nowcasting. We develop an artificial neural network (ANN) framework to predict flare-induced electron density enhancements using ground-based VLF observations from Dehradun and Indore, India (2020–2025). A total of 244 solar flares (157 C-class, 87 M-class) were analyzed. Seven physically motivated inputs: time of day, day of year, VLF amplitude perturbation, reflection height (H′), sharpness factor (<InlineEquation ID="IEq1"> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> <EquationSource Format="TEX">$\beta $</EquationSource> </InlineEquation>), F10.7 flux, and GOES X-ray flux were used to train a feed-forward backpropagation network. The model achieved excellent skill for C-class flares (R = 0.98; MSE = 8.735 × 10<sup>−5</sup>; RMSE&#xa0;= 0.0295; and MAE = 0.0182) and moderate performance for M-class flares (R = 0.74; MSE = 4.06 × 10<sup>−1</sup>; RMSE = 0.63; MAE = 0.47), reflecting increased ionospheric variability under stronger solar forcing. These results demonstrate the robustness of ANN-based approaches for real-time D-region electron density estimation and operational space weather forecasting.</p>

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Artificial Neural Network–based prediction of D-region electron density variations during solar flares using ground-based VLF observations over low-latitude Indian region

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Shivani Chandra,
  • Sudipta Sasmal,
  • Abhirup Datta,
  • Rajesh Singh,
  • Sampad Kumar Panda,
  • Yasuhide Hobara,
  • Ajeet K. Maurya

摘要

The ionospheric D-region (60–90 km) critically controls very low frequency (VLF) radio wave propagation and responds rapidly to solar flare radiation. Accurate prediction of D-region electron density is therefore essential for reliable space weather nowcasting. We develop an artificial neural network (ANN) framework to predict flare-induced electron density enhancements using ground-based VLF observations from Dehradun and Indore, India (2020–2025). A total of 244 solar flares (157 C-class, 87 M-class) were analyzed. Seven physically motivated inputs: time of day, day of year, VLF amplitude perturbation, reflection height (H′), sharpness factor ( β $\beta $ ), F10.7 flux, and GOES X-ray flux were used to train a feed-forward backpropagation network. The model achieved excellent skill for C-class flares (R = 0.98; MSE = 8.735 × 10−5; RMSE = 0.0295; and MAE = 0.0182) and moderate performance for M-class flares (R = 0.74; MSE = 4.06 × 10−1; RMSE = 0.63; MAE = 0.47), reflecting increased ionospheric variability under stronger solar forcing. These results demonstrate the robustness of ANN-based approaches for real-time D-region electron density estimation and operational space weather forecasting.