Ionospheric storm forecasting technique by artificial neural network

Abstract

In this work we further refi ne 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.

Reliable prediction intervals with regression neural networks

“…A number of studies have also been conducted concentrating on the prediction of TEC and other related ionospheric parameters important for telecommunication applications. These studies have dealt with short-term forecasting (Cander et al, 2003;Liu et al, 2005;Koutroumbas et al, 2008;Strangeways et al, 2009;Stamper et al, 2004) and long-term prediction (Maruyama, 2007;Haralambous et al, 2010;Agapitos et al, 2010) of such parameters as well as coping with missing data points (Francis et al, 2001). Furthermore, it is important to note that work in this area is not limited to temporal variations of parameters, it also includes spatial ones (Oyeyemi & Poole, 2004).…”

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

“…For example, Cander et al. (2003) and Wintoft and Cander (2000a, 2000b), developed neural network models to predict the ionospheric storm‐time index foF2. Nakamura et al.…”

“…Furthermore, artificial intelligence techniques have been adopted in ionospheric storm forecasting. For example, Cander et al (2003) and Cander (2000a, 2000b), developed neural network models to predict the ionospheric storm-time index foF2. Nakamura et al (2007) created an operational forecast model of ionospheric variations and storms at Kokubunji using neural networks.…”