Summary of the described ionospheric parameters forecasting based on NN.
|Reference||Approach||Prediction lead time||Main features|
|Sai Gowtam & Tulasi Ram (2017)||ANN prediction model of NmF2 and hmF2 as function of LT, LAT, LONG, SEASON||Climatological model||NmF2 and hmF2 percentage error ranged between 15% and 20%. The NmF2 model performance decreases at low latitudes during the predawn hours and around midnight at middle-high latitude.|
|The hmF2 model performance decreases at low latitude at postsunset.|
|Tulunay et al. (2006)||Regional TEC forecasting model, NN||10 min up to 1 h||Short term forecasting. RMSE increases with forecasting horizon up to about 4 TECU over mid latitude grid points.|
|Habarulema et al. (2011)||ANN regional TEC variability forecasting model as function of time of the day, season, solar and magnetic activity, latitude and longitude||Climatological model||The model accuracy is decreases under geomagnetically disturbed conditions.|
|Huang & Yuan (2014)||TEC single station forecasting model based on BP NN and RBF NN||30 mins||Short term forecasting. RMSE less than 5 TECU, the model performance decreases at lower latitude.|
|Cherrier et al. (2017)||Global TEC maps forecasting model based on recurrent NN and TEC global map by CODE||2–48 h||RMSE increases with forecasting horizon. The model performance decreases under disturbed conditions.|
|Perez (2019)||Global TEC maps forecasting model NN combined with GIM map||24-h or more||Equatorial Ionospheric Anomaly not represented|
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