The probabilistic solar particle event forecasting (PROSPER) model

Papaioannou, Athanasios; Vainio, R.; Raukunen, Osku; Jiggens, Piers; Aran, A.; Dierckxsens, M.; Mallios, Sotirios A.; Paassilta, Miikka; Anastasiadis, A.

doi:10.1051/swsc/2022019

Cited by 9 publications

(3 citation statements)

References 72 publications

(89 reference statements)

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“…The clustering model of the early warning system is the basis for carrying out the classification of new media events. The text clustering sub-model is to categorize previous new media events into a sample set, divide them into several clusters based on some strategy, where the severity of new media events in each cluster is comparable, and find the center of each cluster [23].…”

Section: Analysis Of the Construction Of New Media Event Early Warnin...mentioning

confidence: 99%

Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

Gao,

Zheng

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.

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Section: Analysis Of the Construction Of New Media Event Early Warnin...mentioning

confidence: 99%

Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

Gao,

Zheng

2024

ICST Transactions on Scalable Information Systems

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“…Early empirical models to predict SPEs include: the "proton prediction system" PPS76 (Smart & Shea, 1979, 1989Kahler et al, 2007) based on solar flare parameters (microwave or X-ray flux, flare location); the model (Balch, 1999(Balch, , 2008 used by NOAA Space Weather Prediction Center, which also exploits metric radio type II and type IV bursts (indicating the presence of a coronal Topical Issue -CMEs, ICMEs, SEPs: Observational, Modelling, and Forecasting Advances mass ejection-CME driven shock) as input parameters. Some empirical models incorporate CME information as input (St. Cyr et al, 2017;Papaioannou et al, 2018Papaioannou et al, , 2022, whereas others, such as REleASE (Posner, 2007;Núñez et al, 2018) and UMASEP (Núñez, 2011), rely on the arrival times of relativistic electrons to predict SPEs in the 30-50 MeV energy range or high-energy protons at 1 AU compared to lower >10 MeV energy protons, respectively. Other machine learning-based approaches have been employed such as decision tree models based on GOES soft X-ray (SXR) and high-energy proton observations (Boubrahimi et al, 2017;Núñez & Paul-Pena, 2020;Lavasa et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Upgrades of the ESPERTA forecast tool for solar proton events

Laurenza,

Stumpo,

Zucca

et al. 2024

J. Space Weather Space Clim.

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The Empirical model for Solar Proton Events Real Time Alert (ESPERTA) exploits three solar parameters (flare longitude, soft X-ray fluence, and radio fluence) to provide a timely prediction for the occurrence of solar proton events (SPEs, i.e., when the $>$10MeV proton flux is $\geq$10 pfu) after the emission of a $\geq$ M2 flare. In addition, it makes a prediction for the more geoeffective SPEs for which the $>$10 MeV proton flux is $\geq$ 100 pfu. In this paper, we study two different ways to upgrade the ESPERTA model and implement it in real time: 1) by using ground based observations from the LOFAR stations; 2) by applying a novel machine learning algorithm to flare-based parameters to provide early warnings of SPE occurrence together with a fine-tuned radiation storm level. As a last step, we perform a preliminary study using a neural network to forecast the proton flux profile to complement the ESPERTA tool. We evaluate the models over flare and SPE data the last two solar cycles and discuss the performance and the limits and advantages of the three methods.

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“…An excellent review of SEP models and predictive efforts was recently published by Whitman et al (2022), which summarizes the majority of the existing models. For instance, Papaioannou et al (2022) introduced the Probabilistic Solar Particle Event Forecasting (PROSPER) model, which is incorporated into the Advanced Solar Particle Event Casting System (ASPECS) 1 . The PROSPER model utilizes a Bayesian approach and data-driven methodology to probabilistically predict SEP events for three integral energy channels >10, >30, and >100 MeV.…”

Section: Introductionmentioning

confidence: 99%

Forecasting solar energetic proton integral fluxes with bi-directional long short-term memory neural networks

Nedal,

Kozarev,

Arsenov

et al. 2023

J. Space Weather Space Clim.

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Solar energetic particles are mainly protons and originate from the Sun during solar flares or coronal shock waves. Forecasting the Solar Energetic Protons (SEP) flux is critical for several operational sectors, such as communication and navigation systems, space exploration missions, and aviation flights, as the hazardous radiation may endanger astronauts’, aviation crew and passengers’ health, the delicate electronic components of satellites, space stations, and ground power stations. Therefore, the prediction of the SEP flux is of high importance to our lives and may help mitigate the negative impacts of one of the serious space weather transient phenomena on the near-Earth space environment. Numerous SEP prediction models are being developed with a variety of approaches, such as empirical models, probabilistic models, physics-based models, and AI-based models. In this work, we use the bi-directional long short-term memory (BiLSTM) neural network model architecture to train SEP forecasting models for 3 standard integral GOES channels ($>$10 MeV, $>$30 MeV, $>$60 MeV) with 3 forecast windows (1-day, 2-day, and 3-day ahead) based on daily data obtained from the OMNIWeb database from 1976 to 2019. As the SEP variability is modulated by the solar cycle, we select input parameters that capture the short-term, typically within a span of a few hours, and long-term, typically spanning several days, fluctuations in solar activity. We take the F10.7 index, the sunspot number, the time series of logarithm of the x-ray flux, the solar wind speed, and the average strength of the interplanetary magnetic field as input parameters to our model. The results are validated with an out-of-sample testing set and benchmarked with other types of models.

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The probabilistic solar particle event forecasting (PROSPER) model

Cited by 9 publications

References 72 publications

Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

Upgrades of the ESPERTA forecast tool for solar proton events

Forecasting solar energetic proton integral fluxes with bi-directional long short-term memory neural networks

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