@conference{283, keywords = {Space weather, Input parameter selection, Neural network}, author = {Stefan Lotz and Jacques Beukes and Marelie Davel}, title = {Input parameter ranking for neural networks in a space weather regression problem}, abstract = {Geomagnetic storms are multi-day events characterised by significant perturbations to the magnetic field of the Earth, driven by solar activity. Numerous efforts have been undertaken to utilise in-situ measurements of the solar wind plasma to predict perturbations to the geomagnetic field measured on the ground. Typically, solar wind measurements are used as input parameters to a regression problem tasked with predicting a perturbation index such as the 1-minute cadence symmetric-H (Sym-H) index. We re-visit this problem, with two important twists: (i) An adapted feedforward neural network topology is designed to enable the pairwise analysis of input parameter weights. This enables the ranking of input parameters in terms of importance to output accuracy, without the need to train numerous models. (ii) Geomagnetic storm phase information is incorporated as model inputs and shown to increase performance. This is motivated by the fact that different physical phenomena are at play during different phases of a geomagnetic storm.}, year = {2019}, journal = {South African Forum for Artificial Intelligence Research (FAIR)}, chapter = {133-144}, publisher = {CEUR workshop proceedings}, address = {Cape Town, South Africa}, }