A Survey of Uncertainty Quantification in Machine Learning for Space Weather Prediction

Geosciences

Talha Siddique¹  Md Shaad Mahmud¹  Amy M. Keesee²  Hyunju Connor³  Chigomezyo M. Ngwira⁴ 

[1] Department of Electrical and Computer Engineering, University of New Hampshire, Durham, NH 03824, USA;Department of Physics & Astronomy and Space Science Center, University of New Hampshire, Durham, NH 03824, USA;Department of Physics and Geophysical Institute, University of Alaska-Fairbanks, Fairbanks, AK 99775, USA;Science Group, Atmospheric and Space Technology Research Associate, Louisville, CO 80027, USA;

关键词: artificial intelligence;    uncertainty quantification;    deep learning;    machine learning;    bayesian statistics;   

DOI  :  10.3390/geosciences12010027

来源: DOAJ

【 摘 要 】

With the availability of data and computational technologies in the modern world, machine learning (ML) has emerged as a preferred methodology for data analysis and prediction. While ML holds great promise, the results from such models are not fully unreliable due to the challenges introduced by uncertainty. An ML model generates an optimal solution based on its training data. However, if the uncertainty in the data and the model parameters are not considered, such optimal solutions have a high risk of failure in actual world deployment. This paper surveys the different approaches used in ML to quantify uncertainty. The paper also exhibits the implications of quantifying uncertainty when using ML by performing two case studies with space physics in focus. The first case study consists of the classification of auroral images in predefined labels. In the second case study, the horizontal component of the perturbed magnetic field measured at the Earth’s surface was predicted for the study of Geomagnetically Induced Currents (GICs) by training the model using time series data. In both cases, a Bayesian Neural Network (BNN) was trained to generate predictions, along with epistemic and aleatoric uncertainties. Finally, the pros and cons of both Gaussian Process Regression (GPR) models and Bayesian Deep Learning (DL) are weighed. The paper also provides recommendations for the models that need exploration, focusing on space weather prediction.

【 授权许可】

Unknown