ENSEMBLE MACHINE LEARNING IN SPACE WEATHER ANALYTICS

Abstract

This dissertation addresses several important space weather problems using ensemble learning techniques. An ensemble method combining multiple machine learning models is often more accurate than the individual machine learning models that form the ensemble method. There are several techniques for constructing an ensemble. With in-depth case studies, the dissertation demonstrates the usefulness and effectiveness of ensemble machine learning for space weather analytics, especially for predicting extreme space weather events such as coronal mass ejections (CMEs). The dissertation begins with an ensemble method for predicting the arrival time of CMEs from the Sun to Earth. The proposed method, named CMETNet, combines classical machine learning algorithms such as support vector regression, random forests, XGBoost and Gaussian process regression, along with a deep convolutional neural network (CNN), to perform multimodal learning. The classical machine learning algorithms are used to learn latent patterns from CME features and background solar wind parameters while the deep CNN is used to learn patterns hidden in CME images where the learned patterns are jointly used to make predictions. Experimental results show that CMETNet outperforms existing models, both machine learning based and physics based. Finally, the dissertation presents a fusion method, named DeepCME, to estimate two important properties of CMEs, namely, CME mass and kinetic energy. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. The fusion model extracts features from Large Angle and Spectrometric Coronagraph (LASCO) C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. To the best of current knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations. DeepCME can help scientists better understand CME dynamics. In conclusion, the dissertation showcases many applications of learning techniques including ensemble learning, deep learning, transfer learning and multimodal learning in space weather analytics. The tools and methods developed from the dissertation will make contributions to the understanding and forecasting of CME dynamics and CME geoeffectiveness.