scholarly journals Solar Energetic Particle Event occurrence prediction using Solar Flare Soft X-ray measurements and Machine Learning

2021 ◽  
Author(s):  
Sigiava Aminalragia-Giamini ◽  
Savvas Raptis ◽  
Anastasios Anastasiadis ◽  
Antonis Tsigkanos ◽  
Ingmar Sandberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Solar Flares ◽  
Energetic Particle ◽  
Real Life ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
X Rays ◽  
The Neural Networks ◽  
Event Occurrence

The prediction of the occurrence of Solar Energetic Particle (SEP) events has been investigated over many years and multiple works have presented significant advances in this problem. The accurate and timely prediction of SEPs is of interest to the scientific community as well as mission designers, operators, and industrial partners due to the threat SEPs pose to satellites, spacecrafts and crewed missions. In this work we present a methodology for the prediction of SEPs from the soft X-rays of solar flares associated with SEPs that were measured in 1 AU. We use an expansive dataset covering 25 years of solar activity, 1988-2013, which includes thousands of flares and more than two hundred identified and catalogued SEPs. Neural networks are employed as the predictors in the model providing probabilities for the occurrence or not of an SEP which are converted to yes/no predictions. The neural networks are designed using current and state-of the-art tools integrating recent advances in the machine learning field. The results of the methodology are extensively evaluated and validated using all the available data and it is shown that we achieve very good levels of accuracy with correct SEP occurrence prediction higher than 85% and correct no-SEP predictions higher than 92%. Finally we discuss further work towards potential improvements and the applicability of our model in real life conditions.

scholarly journals Solar flares, coronal mass ejections and solar energetic particle event characteristics

2016 ◽  
Vol 6 ◽  
pp. A42 ◽  
Author(s):  
Athanasios Papaioannou ◽  
Ingmar Sandberg ◽  
Anastasios Anastasiadis ◽  
Athanasios Kouloumvakos ◽  
Manolis K. Georgoulis ◽  
...  
Keyword(s):  
Coronal Mass Ejections ◽  
Solar Flares ◽  
Energetic Particle ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
Particle Event

Ionospheric and stratospheric electric field responses to an extreme solar energetic particle event

2020 ◽  
Author(s):  
EDGAR Andrew BERING ◽  
Robert H. Holzworth ◽  
Michael McCarthy ◽  
Michael Kokorowski ◽  
Robyn M Millan ◽  
...  
Keyword(s):  
Electric Field ◽  
Energetic Particle ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
Particle Event

scholarly journals Rapid fluctuations of stratospheric electric field following a solar energetic particle event

2006 ◽  
Vol 33 (20) ◽  
Author(s):  
M. Kokorowski ◽  
J. G. Sample ◽  
R. H. Holzworth ◽  
E. A. Bering ◽  
S. D. Bale ◽  
...  
Keyword(s):  
Electric Field ◽  
Energetic Particle ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
Particle Event

scholarly journals Parker Solar Probe’s Measurements of the 29 November 2020 Solar Energetic Particle Event

2021 ◽  
Author(s):  
Christina Cohen ◽  
E.R. Christian ◽  
A.C. Cummings ◽  
A.J. Davis ◽  
M.I. Desai ◽  
...  
Keyword(s):  
Energetic Particle ◽  
Solar Energetic Particle ◽  
Solar Energetic Particle Event ◽  
Particle Event

A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

2020 ◽  
pp. 146-162
Author(s):  
Hesham M. Al-Ammal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Anomaly Detection ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Learning Techniques ◽  
Vector Machines

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

scholarly journals Prediction Skill of Extended Range 2-m Maximum Air Temperature Probabilistic Forecasts Using Machine Learning Post-Processing Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Ting Peng ◽  
Xiefei Zhi ◽  
Yan Ji ◽  
Luying Ji ◽  
Ye Tian
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Air Temperature ◽  
Post Processing ◽  
Learning Methods ◽  
Processing Methods ◽  
Machine Learning Methods ◽  
Extended Range ◽  
The Neural Networks ◽  
Maximum Air Temperature

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances.

scholarly journals Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 95 ◽  
Author(s):  
Jacky H. L. Poon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Life Insurance ◽  
Predictive Power ◽  
Real Life ◽  
Model Framework ◽  
Key Stakeholders ◽  
Machine Learning Model ◽  
Loss Reserving ◽  
Risk Pricing

In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size.

scholarly journals An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

2020 ◽  
Vol 14 ◽  
Author(s):  
Yaqing Zhang ◽  
Jinling Chen ◽  
Jen Hong Tan ◽  
Yuxuan Chen ◽  
Yunyi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Real Life ◽  
Learning Rate ◽  
Learning Models ◽  
Learning Methods ◽  
Time Frequency ◽  
Comparison Results

Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

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