Assessing the performance of artificial neural networks to predict ionospheric TEC over Nigeria during different space weather events
Bibliographic record
Abstract
The ionosphere model is essential to satellite-based systems to accurately correct the ionospheric error encountered by satellite signals en route. The Levenberg–Marquardt backpropagation (LMBP) algorithm in the artificial neural network (ANN) was used in this work to predict the total electron content (TEC) within the trough of equatorial ionization anomaly (EIA) over Nigeria. Two sets of data were used over the period of three consecutive years (2011–2013) of high solar activity. The first set was used as an input to the ANN model and the second set of data was used as a target. Seventy percent of the data sets were used to train the network, 15% of the data were used for validation, and 15% used for testing. The performance of the model was assessed during specific quiet and disturbed geomagnetic conditions. The regression analysis of the model output was optimized by minimizing a cost function of the mean square error (MSE). The results of the errors, regression, and comparative analyses have revealed that the ANN model is able to predict accurate and reliable TEC that compares well with the actual experimental data at any geophysical conditions. Hence, this model would be useful to forecast TEC over Nigeria to a reliable threshold.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".