Ionospheric storm forecasting technique by artificial neural network

L. R. Cander, M. M. Milosavljevic´, S. Tomasevic´

Abstract


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.

Keywords


prediction and forecasting;neural networks;ionospheric storms modelling;space weather

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References


DOI: https://doi.org/10.4401/ag-4371
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Published by INGV, Istituto Nazionale di Geofisica e Vulcanologia - ISSN: 2037-416X