Abstract Space weather forecasting is very important, and the prediction of space weather,
especially for solar flares, has increasingly attracted research interests with the numerous …

We propose a hybrid Convolutional Neural Network (CNN) model (Model 2) and modify a
popular CNN model (Model 1) to predict multiclass solar flare occurrence within 24 hr. We …

Solar flare formation mechanisms and their corresponding predictions have commonly been
difficult topics in solar physics for decades. The traditional forecasting method manually …

We developed a solar flare prediction model using a deep neural network (DNN) named
Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the …

We developed an operational solar flare prediction model using deep neural networks,
named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two …

In this study, we present a visual explanation of a deep learning solar flare forecast model
and its relationship to physical parameters of solar active regions (ARs). For this, we use full …

We developed a reliable probabilistic solar-flare forecasting model using a deep neural
network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum …

ABSTRACT We develop a Hybrid Bidirectional Long and Short-Term Memory based on
attention mechanism (HBiLSTM-Attention) model and a BiLSTM-Attention model for …

Prediction of solar flares is a challenging problem in space weather forecasting that has
piqued the interest of many researchers in recent years due to improved data availability …

A deep learning network, long short-term memory (LSTM), is used to predict whether an
active region (AR) will produce a flare of class Γ in the next 24 hr. We consider Γ to be≥ M …