Related papers: Solar Flare Forecast: A Comparative Analysis of Ma…

Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by…

Generation and load balance is required in the economic scheduling of generating units in the smart grid. Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated…

A convolutional neural network (CNN) is used to construct a new catalog for solar flares based on high resolution (1-s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data. The CNN is trained to identify flare…

Solar flares are explosive releases of magnetic energy. Hard X-ray (HXR) flare emission originates from both hot (millions of Kelvin) plasma and nonthermal accelerated particles, giving insight into flare energy release. The Nuclear…

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research due to their complex origins and highly…

Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40…

In this dataset we provide a comprehensive collection of magnetograms (images quantifying the strength of the magnetic field) from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset…

We investigate the use of Long Short-Term Memory (LSTM) and Decomposition-LSTM (DLSTM) networks, combined with an ensemble algorithm, to predict solar flare occurrences using time-series data from the GOES catalog. The dataset spans from…

Efficient prediction of solar flares relies on parameters that quantify the eruptive capability of solar active regions. Several such quantitative predictors have been proposed in the literature, inferred mostly from photospheric…

A demonstrated failure mode for operational solar flare forecasting is the inability to forecast flares that occur near, or just beyond, the solar limb. To address this shortcoming, we develop a "4pi" full-heliosphere event forecasting…

The prediction of solar flares is typically formulated as a binary classification task, distinguishing events as either Flare (FL) or No-Flare (NF) according to a specified threshold (for example, greater than or equal to C-class, M-class,…

Atmospheric neutral density is a crucial component to accurately predict and track the motion of satellites. During periods of elevated solar and geomagnetic activity atmospheric neutral density becomes highly variable and dynamic. This…

Although large volumes of solar data are available for study, the vast majority of these data remain unlabeled and are therefore not amenable to supervised machine learning methods. Having a way to accurately and automatically classify…

The spatial distribution of energetic protons contributes towards the understanding of magnetospheric dynamics. Based upon 17 years of the Cluster/RAPID observations, we have derived machine learning-based models to predict the proton…

Solar flares are a fundamental component of solar eruptive events (SEEs; along with solar energetic particles, SEPs, and coronal mass ejections, CMEs). Flares are the first component of the SEE to impact our atmosphere, which can set the…

This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model…

A Bayesian approach to solar flare prediction has been developed, which uses only the event statistics of flares already observed. The method is simple, objective, and makes few ad hoc assumptions. It is argued that this approach should be…

Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magnetosphere has space-weather consequences such as geomagnetic…

Solar flares occur due to the sudden release of energy stored in active-region magnetic fields. To date, the pre-cursors to flaring are still not fully understood, although there is evidence that flaring is related to changes in the…

The prediction of solar flares, eruptions, and high energy particle storms is of great societal importance. The data mining approach to forecasting has been shown to be very promising. Benchmark datasets are a key element in the further…