Yazar "Wang, Haimin" seçeneğine göre listele

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    An investigation of the causal relationship between sunspot groups and coronal mass ejections by determining source active regions
    (Royal Astronomical Society, 2021) Raheem, Abd-ur; Çavuş, Hüseyin; Çoban, Gani Çağlar; Kınacı, Ahmet Cumhur; Wang, Haimin; Wang, Jason T. L.
    Although the source active regions of some coronal mass ejections (CMEs) were identified in CME catalogues, vast majority of CMEs do not have an identified source active region. We propose a method that uses a filtration process and machine learning to identify the sunspot groups associated with a large fraction of CMEs and compare the physical parameters of these identified sunspot groups with properties of their corresponding CMEs to find mechanisms behind the initiation of CMEs. These CMEs were taken from the Coordinated Data Analysis Workshops (CDAW) data base hosted at NASA's website. The Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs) were taken from the Stanford University's Joint Science Operations Center (JSOC) data base. The source active regions of the CMEs were identified by the help of a custom filtration procedure and then by training a long short-term memory network (LSTM) to identify the patterns in the physical magnetic parameters derived from vector and line-of-sight magnetograms. The neural network simultaneously considers the time series data of these magnetic parameters at once and learns the patterns at the onset of CMEs. This neural network was then used to identify the source HARPs for the CMEs recorded from 2011 till 2020. The neural network was able to reliably identify source HARPs for 4895 CMEs out of 14604 listed in the CDAW data base during the aforementioned period.
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    Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models
    (Iop Publishing Ltd, 2023) Alobaid, Khalid A.; Abduallah, Yasser; Wang, Jason T. L.; Wang, Haimin; Fan, Shen; Li, Jialiang; Cavus, Huseyin
    Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory. We use LASCO C2 data in the period between 1996 January and 2020 December to train, validate, and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.
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    Predicting CME arrival time through data integration and ensemble learning
    (Frontiers Media Sa, 2022) Alobaid, Khalid A.; Abduallah, Yasser; Wang, Jason T. L.; Wang, Haimin; Jiang, Haodi; Xu, Yan; Yurchyshyn, Vasyl
    The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.
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    Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning
    (Springer, 2024) Alobaid, Khalid A.; Wang, Jason T. L.; Wang, Haimin; Jing, Ju; Abduallah, Yasser; Wang, Zhenduo; Farooki, Hameedullah
    The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness are crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth's magnetosphere during which the minimum Dst index value is less than -50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT, and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew's correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.
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    Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification
    (Amer Geophysical Union, 2024) Abduallah, Yasser; Alobaid, Khalid A.; Wang, Jason T. L.; Wang, Haimin; Jordanova, Vania K.; Yurchyshyn, Vasyl; Cavus, Huseyin
    We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot. In the past several years, machine learning and its subfield, deep learning, have attracted considerable interest. Computer vision, natural language processing, and social network analysis make extensive use of machine learning algorithms. Recent applications of these algorithms include the prediction of solar flares and the forecasting of geomagnetic indices. In this paper, we propose an innovative machine learning method that utilizes a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters to provide short-term predictions of the SYM-H index. In addition, we present techniques for quantifying both data and model uncertainties in the output of the proposed method. SYMHnet is a novel deep learning method for making short-term predictions of the SYM-H index (1 or 2 hr in advance) With Bayesian inference, SYMHnet can quantify both aleatoric (data) and epistemic (model) uncertainties in making its prediction SYMHnet generally performs better than related machine learning methods for SYM-H forecasting