Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models
dc.contributor.author | Simms, L. E. | |
dc.contributor.author | Ganushkina, N. Yu. | |
dc.contributor.author | Kamp, M. | |
dc.contributor.author | Balikhin, M. | |
dc.contributor.author | Liemohn, M. W. | |
dc.date.accessioned | 2023-06-01T20:47:43Z | |
dc.date.available | 2024-06-01 16:47:38 | en |
dc.date.available | 2023-06-01T20:47:43Z | |
dc.date.issued | 2023-05 | |
dc.identifier.citation | Simms, L. E.; Ganushkina, N. Yu.; Kamp, M.; Balikhin, M.; Liemohn, M. W. (2023). "Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models." Space Weather 21(5): n/a-n/a. | |
dc.identifier.issn | 1542-7390 | |
dc.identifier.issn | 1542-7390 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/176809 | |
dc.description.abstract | We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES-13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. Value-predicting models developed using ARMAX (autoregressive moving average transfer function), RNN (recurrent neural network), or stepwise-reduced regression produced roughly similar results. Including magnetic local time as a categorical variable to describe both the differing levels of flux and the differing influence of parameters improved the models (r as high as 0.814; Heidke Skill Score (HSS) as high as 0.663), however value-predicting models did a poor job at predicting highs and lows. Diagnostic tests are introduced (cubic fit to observation-prediction relationship and Lag1 correlation) that better assess predictions of extremes than single metrics such as root mean square error, mean absolute error, or median symmetric accuracy. Classifier models (RNN and logistic regression) were equally able to predict flux rise above the 75th percentile (HSS as high as 0.667). Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. Only predictors from 1 or 3 hr before were necessary and a detailed description of flux time series behavior was not needed. Stepwise selection of these variables trimmed non-contributing parameters for a more parsimonious and portable logistic regression model that predicted as well as neural network-derived models. We provide a logistic regression model (LL3: LogisticLag3) based on inputs measured 3 hr previous, along with optimal probability thresholds, for future predictions.Plain Language SummaryAs high levels of electrons in the radiation belts can damage satellites, accurate forecasting would be a useful tool. Electron levels can be predicted using information from the solar wind, the interplanetary magnetic field, and indices measuring disturbances in Earth’s magnetic field. We compare several algorithms to produce such models: regression and neural networks that depend on predictors at one or many previous time steps. We find that dependable predictions can be made from a regression model using predictors from only a single previous time step. More sophisticated neural network techniques are not necessary if interaction and nonlinear terms are introduced to the regression.Key PointsRegression models incorporating interaction and quadratic terms predict electron flux as well as neural network modelsThe description of time series behavior by autoregressive moving average transfer function models, while useful for hypothesis testing, is not necessary for predictionMagnetic local time as a predictor improves the models by describing changing flux levels and the differing influence of parameters over the diurnal period | |
dc.publisher | MIT Press | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | electron flux prediction | |
dc.subject.other | precision recall curve | |
dc.subject.other | ROC curve | |
dc.subject.other | recurrent neural network | |
dc.subject.other | logistic regression | |
dc.subject.other | ARMAX | |
dc.title | Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Electrical Engineering | |
dc.subject.hlbtoplevel | Engineering | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/176809/1/swe21494.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/176809/2/swe21494_am.pdf | |
dc.identifier.doi | 10.1029/2022SW003263 | |
dc.identifier.source | Space Weather | |
dc.identifier.citedreference | Roeder, J. L., Chen, M. W., Fennell, J. F., & Friedel, R. ( 2005 ). Empirical models of the low-energy plasma in the inner magnetosphere. Space Weather, 3 ( 12 ). https://doi.org/10.1029/2005SW000161 | |
dc.identifier.citedreference | Pakhotin, I. P., Drozdov, A. Y., Shprits, Y. Y., Boynton, R. J., Subbotin, D. A., & Balikhin, M. A. ( 2014 ). Simulation of high-energy radiation belt electron fluxes using NARMAX-VERB coupled codes. Journal of Geophysical Research: Space Physics, 119 ( 10 ), 8073 – 8086. https://doi.org/10.1002/2014JA020238 | |
dc.identifier.citedreference | Paulikas, G., & Blake, J. ( 1979 ). Effects of the solar wind on magnetospheric dynamics: Energetic electrons at the synchronous orbit. In Quantitative modeling of magnetospheric processes (pp. 180 – 202 ). American Geophysical Union (AGU). https://doi.org/10.1029/GM021p0180 | |
dc.identifier.citedreference | Reeves, G. D., Morley, S. K., Friedel, R. H. W., Henderson, M. G., Cayton, T. E., Cunningham, G., et al. ( 2011 ). On the relationship between relativistic electron flux and solar wind velocity: Paulikas and Blake revisited. Journal of Geophysical Research, 116 ( A2 ), A02213. https://doi.org/10.1029/2010JA015735 | |
dc.identifier.citedreference | Rowland, W., & Weigel, R. S. ( 2012 ). Intracalibration of particle detectors on a three-axis stabilized geostationary platform. Space Weather, 10 ( 11 ). https://doi.org/10.1029/2012SW000816 | |
dc.identifier.citedreference | Saito, T., & Rehmsmeier, M. ( 2015 ). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One, 10 ( 3 ), e0118432. https://doi.org/10.1371/journal.pone.0118432 | |
dc.identifier.citedreference | Schaefer, J. T. ( 1990 ). The critical success index as an indicator of warning skill. Weather and Forecasting, 5 ( 4 ), 570 – 575. https://doi.org/10.1175/1520-0434 | |
dc.identifier.citedreference | Shi, Y., Zesta, E., & Lyons, L. R. ( 2009 ). Features of energetic particle radial profiles inferred from geosynchronous responses to solar wind dynamic pressure enhancements. Annales Geophysicae, 27 ( 2 ), 851 – 859. https://doi.org/10.5194/angeo-27-851-2009 | |
dc.identifier.citedreference | Sicard-Piet, A., Bourdarie, S., Boscher, D., Friedel, R. H. W., Thomsen, M., Goka, T., et al. ( 2008 ). A new international geostationary electron model: IGE-2006, from 1 keV to 5.2 MeV. Space Weather, 6 ( 7 ). https://doi.org/10.1029/2007SW000368 | |
dc.identifier.citedreference | Sillanpää, I., Ganushkina, N. Y., Dubyagin, S., & Rodriguez, J. V. ( 2017 ). Electron fluxes at geostationary orbit from GOES MAGED data. Space Weather, 15 ( 12 ), 1602 – 1614. https://doi.org/10.1002/2017SW001698 | |
dc.identifier.citedreference | Simms, L. E., & Engebretson, M. ( 2020 ). Classifier neural network models predict relativistic electron events at geosynchronous orbit better than multiple regression or ARMAX models. Journal of Geophysical Research: Space Physics, 125 ( 5 ), e2019JA027357. https://doi.org/10.1029/2019JA027357 | |
dc.identifier.citedreference | Simms, L. E., Engebretson, M., Clilverd, M., Rodger, C., Lessard, M., Gjerloev, J., & Reeves, G. ( 2018 ). A distributed lag autoregressive model of geostationary relativistic electron fluxes: Comparing the influences of waves, seed and source electrons, and solar wind inputs. Journal of Geophysical Research: Space Physics, 123 ( 5 ), 3646 – 3671. https://doi.org/10.1029/2017JA025002 | |
dc.identifier.citedreference | Simms, L. E., Engebretson, M., & Reeves, G. ( 2022 ). Removing diurnal signals and longer term trends from electron flux and ULF correlations: A comparison of spectral subtraction, simple differencing, and ARIMAX models. Journal of Geophysical Research, 127 ( 2 ), e2021JA030021. https://doi.org/10.1029/2021JA030021 | |
dc.identifier.citedreference | Simms, L. E., Engebretson, M. J., Clilverd, M. A., Rodger, C. J., & Reeves, G. D. ( 2018 ). Nonlinear and synergistic effects of ULF Pc5, VLF Chorus, and EMIC waves on relativistic electron flux at geosynchronous orbit. Journal of Geophysical Research: Space Physics, 123 ( 6 ), 4755 – 4766. https://doi.org/10.1029/2017JA025003 | |
dc.identifier.citedreference | Simms, L. E., Engebretson, M. J., Pilipenko, V., Reeves, G. D., & Clilverd, M. ( 2016 ). Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis. Journal of Geophysical Research: Space Physics, 121 ( 4 ), 3181 – 3197. https://doi.org/10.1002/2016JA022414 | |
dc.identifier.citedreference | Simms, L. E., Engebretson, M. J., Rodger, C. J., Gjerloev, J. W., & Reeves, G. D. ( 2019 ). Predicting lower band chorus with autoregressive-moving average transfer function (ARMAX) models. Journal of Geophysical Research: Space Physics, 124 ( 7 ), 5692 – 5708. https://doi.org/10.1029/2019ja026726 | |
dc.identifier.citedreference | Simms, L. E., Ganushkina, N. Y., van de Kamp, M., Liemohn, M. W., & Dubyagin, S. ( 2022 ). Using ARMAX models to determine the drivers of 40-150 keV GOES electron fluxes. Journal of Geophysical Research, 127 ( 9 ), e2022JA030538. https://doi.org/10.1029/2022JA030538 | |
dc.identifier.citedreference | Simms, L. E., Pilipenko, V., Engebretson, M. J., Reeves, G. D., Smith, A. J., & Clilverd, M. ( 2014 ). Prediction of relativistic electron flux at geostationary orbit following storms: Multiple regression analysis. Journal of Geophysical Research: Space Physics, 119 ( 9 ), 7297 – 7318. https://doi.org/10.1002/2014JA019955 | |
dc.identifier.citedreference | Smirnov, A. G., Berrendorf, M., Shprits, Y. Y., Kronberg, E. A., Allison, H. J., Aseev, N. A., et al. ( 2020 ). Medium energy electron flux in Earth’s outer radiation belt (MERLIN): A machine learning model. Space Weather, 18 ( 11 ), e2020SW002532. https://doi.org/10.1029/2020SW002532 | |
dc.identifier.citedreference | Smith, G. ( 2018 ). Step away from stepwise. Journal of Big Data, 5 ( 32 ), 32. https://doi.org/10.1186/s40537-018-0143-6 | |
dc.identifier.citedreference | Stepanov, N. A., Sergeev, V. A., Sormakov, D. A., Andreeva, V. A., Dubyagin, S. V., Ganushkina, N., et al. ( 2021 ). Superthermal proton and electron fluxes in the plasma sheet transition region and their dependence on solar wind parameters. Journal of Geophysical Research: Space Physics, 126 ( 4 ), e2020JA028580. https://doi.org/10.1029/2020JA028580 | |
dc.identifier.citedreference | Subbotin, D. A., & Shprits, Y. Y. ( 2009 ). Three-dimensional modeling of the radiation belts using the Versatile Electron Radiation Belt (VERB) code. Space Weather, 7 ( 10 ). https://doi.org/10.1029/2008SW000452 | |
dc.identifier.citedreference | Swiger, B. M., Liemohn, M. W., Ganushkina, N. Y., & Dubyagin, S. ( 2022 ). Energetic electron flux predictions in the near-earth plasma sheet from solar wind driving. Space Weather, 20 ( 11 ), e2022SW003150. https://doi.org/10.1029/2022SW003150 | |
dc.identifier.citedreference | Thomsen, M. F., Henderson, M. G., & Jordanova, V. K. ( 2013 ). Statistical properties of the surface-charging environment at geosynchronous orbit. Space Weather, 11 ( 5 ), 237 – 244. https://doi.org/10.1002/swe.20049 | |
dc.identifier.citedreference | Tofallis, C. ( 2015 ). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, 66 ( 8 ), 1352 – 1362. https://doi.org/10.1057/jors.2014.103 | |
dc.identifier.citedreference | Whittingham, M., Stephens, P., Bradbury, R., & Freckleton, R. ( 2006 ). Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75 ( 5 ), 1182 – 1189. https://doi.org/10.1111/j.1365-2656.2006.01141.x | |
dc.identifier.citedreference | Yerushalmy, J. ( 1947 ). Statistical problems in assessing methods of medical diagnosis, with special reference to X-Ray techniques. Public Health Reports, 62 ( 40 ), 1432 – 1449. https://doi.org/10.2307/4586294 | |
dc.identifier.citedreference | Alpaydin, E. ( 2014 ). Introduction to machine learning (Vol. 3 ). MIT Press. | |
dc.identifier.citedreference | Balikhin, M. A., Boynton, R. J., Billings, S. A., Gedalin, M., Ganushkina, N., Coca, D., & Wei, H. ( 2010 ). Data based quest for solar wind-magnetosphere coupling function. Geophysical Research Letters, 37 ( 24 ), L24107. https://doi.org/10.1029/2010GL045733 | |
dc.identifier.citedreference | Balikhin, M. A., Boynton, R. J., Walker, S. N., Borovsky, J. E., Billings, S. A., & Wei, H. L. ( 2011 ). Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit. Geophysical Research Letters, 38 ( 18 ), L18105. https://doi.org/10.1029/2011GL048980 | |
dc.identifier.citedreference | Balikhin, M. A., Rodriguez, J., Boynton, R. J., Walker, S., Aryan, H., Sibeck, D., & Billings, S. ( 2016 ). Comparative analysis of NOAA REFM and SNB3GEO tools for the forecast of the fluxes of high-energy electrons at GEO. Space Weather, 14 ( 1 ), 22 – 31. https://doi.org/10.1002/2015SW001303 | |
dc.identifier.citedreference | Berkson, J. ( 1944 ). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39 ( 227 ), 357 – 365. https://doi.org/10.2307/2280041 | |
dc.identifier.citedreference | Blake, J. B., Baker, D. N., Turner, N., Ogilvie, K. W., & Lepping, R. P. ( 1997 ). Correlation of changes in the outer-zone relativistic-electron population with upstream solar wind and magnetic field measurements. Geophysical Research Letters, 24 ( 8 ), 927 – 929. https://doi.org/10.1029/97GL00859 | |
dc.identifier.citedreference | Boynton, R. J., Amariutei, O. A., Shprits, Y. Y., & Balikhin, M. A. ( 2019 ). The system science development of local time-dependent 40-keV electron flux models for geostationary orbit. Space Weather, 17 ( 6 ), 894 – 906. https://doi.org/10.1029/2018SW002128 | |
dc.identifier.citedreference | Boynton, R. J., Balikhin, M. A., & Billings, S. A. ( 2015 ). Online NARMAX model for electron fluxes at GEO. Annales Geophysicae, 33 ( 3 ), 405 – 411. https://doi.org/10.5194/angeo-33-405-2015 | |
dc.identifier.citedreference | Boynton, R. J., Balikhin, M. A., Billings, S. A., Reeves, G. D., Ganushkina, N., Gedalin, M., et al. ( 2013 ). The analysis of electron fluxes at geosynchronous orbit employing a NARMAX approach. Journal of Geophysical Research: Space Physics, 118 ( 4 ), 1500 – 1513. https://doi.org/10.1002/jgra.50192 | |
dc.identifier.citedreference | Boynton, R. J., Balikhin, M. A., Billings, S. A., Wei, H. L., & Ganushkina, N. ( 2011 ). Using the NARMAX OLS-ERR algorithm to obtain the most influential coupling functions that affect the evolution of the magnetosphere. Journal of Geophysical Research, 116 ( A5 ), A05218. https://doi.org/10.1029/2010JA015505 | |
dc.identifier.citedreference | Boynton, R. J., Balikhin, M. A., Sibeck, D. G., Walker, S. N., Billings, S. A., & Ganushkina, N. ( 2016 ). Electron flux models for different energies at geostationary orbit. Space Weather, 14 ( 10 ), 846 – 860. https://doi.org/10.1002/2016SW001506 | |
dc.identifier.citedreference | Camporeale, E., Wilkie, G. J., Drozdov, A. Y., & Bortnik, J. ( 2022 ). Data-driven discovery of fokker-planck equation for the earth’s radiation belts electrons using physics-informed neural networks. Journal of Geophysical Research: Space Physics, 127, e2022JA030377. https://doi.org/10.1029/2022JA030377 | |
dc.identifier.citedreference | Capman, N. S. S., Simms, L. E., Engebretson, M. J., Clilverd, M. A., Rodger, C. J., Reeves, G. D., et al. ( 2019 ). Comparison of multiple and logistic regression analyses of relativistic electron flux enhancement at geosynchronous orbit following storms. Journal of Geophysical Research: Space Physics, 124 ( 12 ), 10246 – 10256. https://doi.org/10.1029/2019JA027132 | |
dc.identifier.citedreference | Chakraborty, S., & Morley, S. K. ( 2020 ). Probabilistic prediction of geomagnetic storms and the Kp index. Journal of Space Weather and Space Climate, 10, 36. https://doi.org/10.1051/swsc/2020037 | |
dc.identifier.citedreference | Chen, M. W., Lemon, C. L., Orlova, K., Shprits, Y., Hecht, J., & Walterscheid, R. L. ( 2015 ). Comparison of simulated and observed trapped and precipitating electron fluxes during a magnetic storm. Geophysical Research Letters, 42 ( 20 ), 8302 – 8311. https://doi.org/10.1002/2015GL065737 | |
dc.identifier.citedreference | Chicco, D., & Jurman, G. ( 2020 ). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21 ( 6 ), 6. https://doi.org/10.1186/s2864-019-6413-7 | |
dc.identifier.citedreference | Choi, H. S., Lee, J., Cho, K. S., Kwak, Y. S., Cho, I. H., Park, Y. D., et al. ( 2011 ). Analysis of GEO spacecraft anomalies: Space weather relationships. Space Weather, 9 ( 5 ), 1 – 12. https://doi.org/10.1029/2010SW000597 | |
dc.identifier.citedreference | Chu, X., Ma, D., Bortnik, J., Tobiska, A., Tobiska, W. K., Cruz, A., et al. ( 2021 ). Relativistic electron model in the outer radiation belt using a neural network approach. Space Weather, 19 ( 12 ), e2021SW002808. https://doi.org/10.1029/2021SW002808 | |
dc.identifier.citedreference | Denton, M. H., Henderson, M. G., Jordanova, V. K., Thomsen, M. F., Borovsky, J. E., Woodroffe, J., et al. ( 2016 ). An improved empirical model of electron and ion fluxes at geosynchronous orbit based on upstream solar wind conditions. Space Weather, 14 ( 7 ), 511 – 523. https://doi.org/10.1002/2016SW001409 | |
dc.identifier.citedreference | Denton, M. H., Thomsen, M. F., Jordanova, V. K., Henderson, M. G., Borovsky, J. E., Denton, J. S., et al. ( 2015 ). An empirical model of electron and ion fluxes derived from observations at geosynchronous orbit. Space Weather, 13 ( 4 ), 233 – 249. https://doi.org/10.1002/2015SW001168 | |
dc.identifier.citedreference | Efron, B., & Tibshirani, R. ( 1986 ). Boostrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1, 54 – 77. https://doi.org/10.1214/SS/1177013815 | |
dc.identifier.citedreference | Fawcett, T. ( 2006 ). An introduction to ROC analysis. Pattern Recognition Letters, 27 ( 8 ), 861 – 874. https://doi.org/10.1016/j.patrec.2005.10.010 | |
dc.identifier.citedreference | Fok, M.-C., Buzulukova, N. Y., Chen, S.-H., Glocer, A., Nagai, T., Valek, P., & Perez, J. D. ( 2014 ). The comprehensive inner magnetosphere-ionosphere model. Journal of Geophysical Research: Space Physics, 119 ( 9 ), 7522 – 7540. https://doi.org/10.1002/2014JA020239 | |
dc.identifier.citedreference | Freeman, J. W. ( 1974 ). Kp dependence of plasma sheet boundary. Journal of Geophysical Research, 79 ( 28 ), 4315 – 4317. https://doi.org/10.1029/ja079i028p04315 | |
dc.identifier.citedreference | Freeman, J. W., O’Brien, T. P., Chan, A. A., & Wolf, R. A. ( 1998 ). Energetic electrons at geostationary orbit during the November 3-4, 1993 storm: Spatial/temporal morphology, characterization by a power law spectrum and, representation by an artificial neural network. Journal of Geophysical Research, 103 ( A11 ), 26251 – 26260. https://doi.org/10.1029/97JA03268 | |
dc.identifier.citedreference | Ganushkina, N. Y., Liemohn, M. W., Amariutei, O. A., & Pitchford, D. ( 2014 ). Low-energy electrons (5-50 keV) in the inner magnetosphere. Journal of Geophysical Research: Space Physics, 119 ( 1 ), 246 – 259. https://doi.org/10.1002/2013JA019304 | |
dc.identifier.citedreference | Ganushkina, N. Y., Sillanpää, I., Welling, D., Haiducek, J., Liemohn, M., Dubyagin, S., & Rodriguez, J. V. ( 2019 ). Validation of Inner Magnetosphere Particle Transport and Acceleration Model (IMPTAM) with long-term GOES MAGED measurements of keV electron fluxes at geostationary orbit. Space Weather, 17 ( 5 ), 687 – 708. https://doi.org/10.1029/2018SW002028 | |
dc.identifier.citedreference | Ganushkina, N. Y., Swiger, B., Dubyagin, S., Matéo-Vélez, J.-C., Liemohn, M. W., Sicard, A., & Payan, D. ( 2021 ). Worst-case severe environments for surface charging observed at LANL satellites as dependent on solar wind and geomagnetic conditions. Space Weather, 19 ( 9 ), e2021SW002732. https://doi.org/10.1029/2021SW002732 | |
dc.identifier.citedreference | Ginet, G. P., O’Brien, T. P., Huston, S. L., Johnston, W. R., Guild, T. B., Friedel, R., et al. ( 2013 ). AE9, AP9 and SPM: New models for specifying the trapped energetic particle and space plasma environment. Space Science Reviews, 179 ( 1–4 ), 579 – 615. https://doi.org/10.1007/s11214-013-9964-y | |
dc.identifier.citedreference | Glauert, S. A., Horne, R. B., & Meredith, N. P. ( 2014 ). Three-dimensional electron radiation belt simulations using the BAS radiation belt model with new diffusion models for chorus, plasmaspheric hiss, and lightning-generated whistlers. Journal of Geophysical Research: Space Physics, 119 ( 1 ), 268 – 289. https://doi.org/10.1002/2013JA019281 | |
dc.identifier.citedreference | Hartley, D. P., Denton, M. H., & Rodriguez, J. V. ( 2014 ). Electron number density, temperature, and energy density at GEO and links to the solar wind: A simple predictive capability. Journal of Geophysical Research: Space Physics, 119 ( 6 ), 4556 – 4571. https://doi.org/10.1002/2014JA019779 | |
dc.identifier.citedreference | Heidke, P. ( 1926 ). Measures of success and goodness of wind force forecasts by the gale-warning service. Geografiska Annaler, 8 ( 4 ), 301 – 349. https://doi.org/10.1080/20014422.1926.11881138 | |
dc.identifier.citedreference | Hochreiter, S., & Schmidhuber, J. ( 1997 ). Long short-term memory. Neural Computation, 9 ( 8 ), 1735 – 1780. https://doi.org/10.1162/neco.1997.9.8.1735 | |
dc.identifier.citedreference | Hurvich, C. M., & Tsai, C. ( 1990 ). The impact of model selection on inference in linear regression. The American Statistician, 44 ( 3 ), 214 – 217. https://doi.org/10.1080/00031305.1990.10475722 | |
dc.identifier.citedreference | Hyndman, R., & Athanasopoulos, G. ( 2018 ). Forecasting: Principles and practice. Heathmont. | |
dc.identifier.citedreference | Iyemori, T., Takeda, M., Nose, M., Odagi, Y., & Toh, H. ( 2010 ). Mid-latitude geomagnetic indices ASY and SYM for 2009 (provisional). In Internal report of data analysis center for geomagnetism and space magnetism. Kyoto University. | |
dc.identifier.citedreference | Jordanova, V. K., Tu, W., Chen, Y., Morley, S. K., Panaitescu, A.-D., Reeves, G. D., & Kletzing, C. A. ( 2016 ). RAM-SCB simulations of electron transport and plasma wave scattering during the October 2012 double-dip storm. Journal of Geophysical Research: Space Physics, 121 ( 9 ), 8712 – 8727. https://doi.org/10.1002/2016JA022470 | |
dc.identifier.citedreference | Katsavrias, C., Aminalragia-Giamini, S., Papadimitriou, C., Daglis, I. A., Sandberg, I., & Jiggens, P. ( 2022 ). Radiation belt model including semi-annual variation and solar driving (Sentinel). Space Weather, 20 ( 1 ), e2021SW002936. https://doi.org/10.1029/2021SW002936 | |
dc.identifier.citedreference | Kellerman, A. C., & Shprits, Y. Y. ( 2012 ). On the influence of solar wind conditions on the outer-electron radiation belt. Journal of Geophysical Research, 117 ( A5 ). https://doi.org/10.1029/2011JA017253 | |
dc.identifier.citedreference | Koons, H. C., & Gorney, D. J. ( 1991 ). A neural network model of the relativistic electron flux at geosynchronous orbit. Journal of Geophysical Research, 96 ( A4 ), 5549 – 5556. https://doi.org/10.1029/90JA02380 | |
dc.identifier.citedreference | Koons, H. C., Mazur, J. E., Selesnick, R. S., Blake, J. B., Fennell, J. F., Roeder, J. L., & Anderson, P. C. ( 2000 ). The impact of the space environment on space systems. AFRL-VS-TR-20001578. | |
dc.identifier.citedreference | Korth, H., Thomsen, M. F., Borovsky, J. E., & McComas, D. J. ( 1999 ). Plasma sheet access to geosynchronous orbit. Journal of Geophysical Research, 104 ( A11 ), 25047 – 25061. https://doi.org/10.1029/1999JA900292 | |
dc.identifier.citedreference | Lam, H.-L., Boteler, D. H., Burlton, B., & Evans, J. ( 2012 ). Anik-E1 and E2 satellite failures of January 1994 revisited. Space Weather, 10 ( 10 ). https://doi.org/10.1029/2012SW000811 | |
dc.identifier.citedreference | Li, X., Baker, D. N., Temerin, M., Reeves, G., Friedel, R., & Shen, C. ( 2005 ). Energetic electrons, 50 keV to 6 MeV, at geosynchronous orbit: Their responses to solar wind variations. Space Weather, 3 ( 4 ). https://doi.org/10.1029/2004SW000105 | |
dc.identifier.citedreference | Li, X., Temerin, M., Baker, D. N., Reeves, G. D., & Larson, D. ( 2001 ). Quantitative prediction of radiation belt electrons at geostationary orbit based on solar wind measurements. Geophysical Research Letters, 28 ( 9 ), 1887 – 1890. https://doi.org/10.1029/2000GL012681 | |
dc.identifier.citedreference | Liemohn, M. W., Adam, J. G., & Ganushkina, N. Y. ( 2022 ). Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve. Space Weather. e2022SW003102. https://doi.org/10.1029/2022SW003102 | |
dc.identifier.citedreference | Liemohn, M. W., Azari, A. R., Ganushkina, N. Y., & Rastaetter, L. ( 2020 ). The STONE curve: A ROC-derived model performance assessment tool. Earth and Space Science, 7 ( 8 ), e2020EA001106. https://doi.org/10.1029/2020EA001106 | |
dc.identifier.citedreference | Liemohn, M. W., Shane, A. D., Azari, A. R., Petersen, A. K., Swiger, B. M., & Mukhopadhyay, A. ( 2021 ). RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics. Journal of Atmospheric and Solar-Terrestrial Physics, 218, 105624. https://doi.org/10.1016/j.jastp.2021.105624 | |
dc.identifier.citedreference | Ling, A. G., Ginet, G. P., Hilmer, R. V., & Perry, K. L. ( 2010 ). A neural network-based geosynchronous relativistic electron flux forecasting model. Space Weather, 8 ( 9 ). https://doi.org/10.1029/2010SW000576 | |
dc.identifier.citedreference | Loto’aniu, T. M., Singer, H. J., Rodriguez, J. V., Green, J., Denig, W., Biesecker, D., & Angelopoulos, V. ( 2015 ). Space weather conditions during the Galaxy 15 spacecraft anomaly. Space Weather, 13 ( 8 ), 484 – 502. https://doi.org/10.1002/2015SW001239 | |
dc.identifier.citedreference | Lyatsky, W., & Khazanov, G. V. ( 2008 ). Effect of solar wind density on relativistic electrons at geosynchronous orbit. Geophysical Research Letters, 35 ( 3 ), L03109. https://doi.org/10.1029/2007GL032524 | |
dc.identifier.citedreference | Ma, D., Chu, X., Bortnik, J., Claudepierre, S. G., Tobiska, W. K., Cruz, A., et al. ( 2022 ). Modeling the dynamic variability of sub-relativistic outer radiation belt electron fluxes using machine learning. Space Weather, 20, e2022SW003079. https://doi.org/10.1029/2022SW003079 | |
dc.identifier.citedreference | Matéo-Vélez, J.-C., Sicard, A., Payan, D., Ganushkina, N., Meredith, N. P., & Sillanpäa, I. ( 2018 ). Spacecraft surface charging induced by severe environments at geosynchronous orbit. Space Weather, 16 ( 1 ), 89 – 106. https://doi.org/10.1002/2017SW001689 | |
dc.identifier.citedreference | Morley, S. K., Brito, T. V., & Welling, D. T. ( 2018 ). Measures of model performance based on the log accuracy ratio. Space Weather, 16 ( 1 ), 69 – 88. https://doi.org/10.1002/2017SW001669 | |
dc.identifier.citedreference | Mundry, R., & Nunn, C. ( 2009 ). Stepwise model fitting and statistical inference: Turning noise into signal pollution. The American Naturalist, 173 ( 1 ), 119 – 123. https://doi.org/10.1086/593303 | |
dc.identifier.citedreference | Neter, J., Kutner, M. H., & Wassermann, W. ( 1990 ). Applied linear statistical models ( 3rd ed. ). Irwin. | |
dc.working.doi | NO | en |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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