Uncertainty Quantification for Machine Learning-Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

COMPUTATIONAL GEOPHYSICS ; Neural networks, fuzzy logic, machine learning ; EXPLORATION GEOPHYSICS ; Gravity methods ; GEODESY AND GRAVITY ; Transient deformation ; Tectonic deformation ; Time variable gravity ; Gravity anomalies and Earth structure ; Satellite geodesy: results ; Seismic cycle related deformations ; HYDROLOGY ; Estimation and forecasting ; Uncertainty assessment ; INFORMATICS ; Machine learning ; Forecasting ; Uncertainty ; IONOSPHERE ; Modeling and forecasting ; MAGNETOSPHERIC PHYSICS ; MATHEMATICAL GEOPHYSICS ; Probabilistic forecasting ; Uncertainty quantification ; Prediction ; OCEANOGRAPHY: GENERAL ; Ocean predictability and prediction ; NATURAL HAZARDS ; Monitoring, forecasting, prediction ; POLICY SCIENCES ; RADIO SCIENCE ; Interferometry ; Ionospheric physics ; SEISMOLOGY ; Continental crust ; Earthquake dynamics ; Earthquake source observations ; Earthquake interaction, forecasting, and prediction ; Seismicity and tectonics ; Subduction zones ; SPACE WEATHER ; Policy ; Research Article ; machine learning ; uncertainty quantification ; confidence intervals ; probabilistic ionosphere forecast ; space weather ; ensemble ; Bayesian neural network ; quantile gradient boosting     «

COMPUTATIONAL GEOPHYSICS ; Neural networks, fuzzy logic, machine learning ; EXPLORATION GEOPHYSICS ; Gravity methods ; GEODESY AND GRAVITY ; Transient deformation ; Tectonic deformation ; Time variable gravity ; Gravity anomalies and Earth structure ; Satellite geodesy: results ; Seismic cycle related deformations ; HYDROLOGY ; Estimation and forecasting ; Uncertainty assessment ; INFORMATICS ; Machine learning ; Forecasting ; Uncertainty ; IONOSPHERE ; Modeling and forecastin...     »