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ThinkMind // International Journal On Advances in Telecommunications, volume 10, numbers 1 and 2, 2017 // View article tele_v10_n12_2017_2


Impact of Analytics and Meta-learning on Estimating Geomagnetic Storms: A Two-stage Framework for Prediction

Authors:
Taylor K. Larkin
Denise J. McManus

Keywords: ensemble modeling; space weather; quantile regression; stacked generalization; telecommunications

Abstract:
Cataclysmic damage to telecommunication infrastructures, from power grids to satellites, is a global concern. Natural disasters, such as hurricanes, tsunamis, floods, mud slides, and tornadoes have impacted telecommunication services while costing millions of dollars in damages and loss of business. Geomagnetic storms, specifically coronal mass ejections, have the same risk of imposing catastrophic devastation as other natural disasters. With increases in data availability, accurate predictions can be made using sophisticated ensemble modeling schemes. In this work, one such scheme, referred to as stacked generalization, is used to predict a geomagnetic storm index value associated with 2,811 coronal mass ejection events that occurred between 1996 and 2014. To increase lead time, two rounds (stages) of stacked generalization using data relevant to a coronal mass ejection's life span are executed. Results show that for this dataset, stacked generalization performs significantly better than using a single model in both stages for the most important error metrics. In addition, overall variable importance scores for each predictor variable can be calculated from this ensemble strategy. Utilizing these importance scores can help aid telecommunication researchers in studying the significant drivers of geomagnetic storms while also maintaining predictive accuracy.

Pages: 11 to 21

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: June 30, 2017

Published in: journal

ISSN: 1942-2601

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