Ionospheric storm forecasting technique by artificial neural network

• Main Authors: S. Tomasevic´, M. M. Milosavljevic´, L. R. Cander
• Format: Article
• Language: English
• Published: Istituto Nazionale di Geofisica e Vulcanologia (INGV) 2003-06-01
• Series: Annals of Geophysics
• Subjects: prediction and forecasting; neural networks; ionospheric storms modelling; space weather
• Online Access: http://www.annalsofgeophysics.eu/index.php/annals/article/view/4371

In this work we further refine and improve the neural network based ionospheric characteristic's foF2 predictor,
 which is actually a neural network autoregressive model with additional input signals (NNARX). Our analysis
 is focused on choice of X parts of NNARX model in order to capture middle and long term dependencies. Daily
 distribution of prediction error suggests need for structural changes of the neural network model, as well as
 adaptation of running average lengths used for determination of X inputs. Generalisation properties of proposed
 neural predictor are improved by carefully designed pruning procedure with additional regularisation term in
 criterion function. Some results from the NNARX model are presented to illustrate the feasibility of using such
 a model as ionospheric storm forecasting technique.