Interpretability of deep neural networks for SYM-H prediction

Abstract

Deep neural networks (DNNs) have shown impressive performance on a wide variety of applications, but it remains di cult to interpret these models. For regression modelling, DNNs generally do not explicitly provide any information about the utility of each of the input parameters in terms of their contribution to model accuracy. With this in mind, we develop the pairwise network, an adaptation to the multilayer perceptron (MLP) that allows the ranking of input parameters according to their contribution to model output. The technique is developed using synthetic data, before its application is demonstrated in the context of a space physics problem. Geomagnetic storms are multi-day events characterised by signi cant perturbations to the magnetic eld of the Earth, driven by solar activity. Previous storm forecasting e orts typically use solar wind measurements 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 the task of predicting SYM-H from solar wind parameters, with two `twists': (i) Geomagnetic storm phase information is incorporated as model inputs and shown to increase prediction performance. (ii) We describe the pairwise network structure and training process { rst validating ranking ability on synthetic data, before using the network to analyse the SYM-H problem. We found that the proposed pairwise network achieved slightly better performance than an MLP on this task while providing feature rankings that correspond to our understanding of the underlying physics.