Forecast of Solar Proton Events With Noaa Scales Based on Solar X-Ray Flare Data Using Neural Network

Abstract

Abstract:In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

Prediction of Solar Proton Events with Machine Learning: Comparison with Operational Forecasts and "All-Clear" Perspectives

“…Kim et al (2018) developed an algorithm to predict SPEs using the solar radio flux by employing statistical analysis, neural networks, and genetic algorithms. Jeong et al (2014) developed an algorithm of SPE forecasting with NOAA scales based on Multi-Layer Perceptrons (MLPs) and using GOES solar soft X-ray (SXR) characteristics of flares from 1976 to 2011. Bain et al (2019) pointed that ML models built on the data available in real-time could outperform operational forecasts.…”

“…This approach has been validated over several solar cycles. Despite advances in ML-based predictions, comparisons with these operational SWPC NOAA forecasts or application to the same temporal and spatial scales (Zhong et al 2019;Jeong et al 2014;Bain et al 2019) are rarely made.…”