Ensemble Learning for CME Arrival Time Prediction

• URL: http://arxiv.org/abs/2305.00258v1
• Date: Sat, 29 Apr 2023 13:35:43 GMT
• Title: Ensemble Learning for CME Arrival Time Prediction
• Authors: Khalid A. Alobaid, Jason T. L. Wang
• Abstract summary: An Earth-directed coronal mass ejection (CME) can cause serious consequences to the human system. We propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth.
• Score: 2.055949720959582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 hours.

Related papers

• Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning [0.7545833157486898]
  Deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. GeoCME is trained on observations from the instruments including LASCO C2, EIT and MDI on board the Solar and Heliospheric Observatory. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations.
  arXiv  Detail & Related papers  (2025-01-02T02:24:37Z)
• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models [1.2126495348848583]
  We propose a new method, called DeepCME, to estimate two properties of Coronal mass ejections (CMEs) DeepCME is a fusion of three deep learning models, including ResNet, InceptionNet, and InceptionResNet. To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.
  arXiv  Detail & Related papers  (2023-12-04T07:25:55Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Physics-driven machine learning for the prediction of coronal mass ejections' travel times [46.58747894238344]
  Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are correlated to geomagnetic storms and may induce the generation of Solar Energetic Particles streams. The present paper introduces a physics-driven artificial intelligence approach to the prediction of CMEs travel time.
  arXiv  Detail & Related papers  (2023-05-17T08:53:29Z)
• Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids [58.720142291102135]
  We present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training.
  arXiv  Detail & Related papers  (2022-06-23T15:27:29Z)
• Predicting the Geoeffectiveness of CMEs Using Machine Learning [0.0]
  This work focuses on experimenting with different machine learning methods trained on white-light coronagraph datasets. We develop binary classification models using logistic regression, K-Nearest Neighbors, Support Vector Machines, feed forward artificial neural networks, as well as ensemble models. We discuss the main challenges of this task, namely the extreme imbalance between the number of geoeffective and ineffective events in our dataset.
  arXiv  Detail & Related papers  (2022-06-23T03:56:22Z)
• Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network [6.759687230043489]
  Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general. We propose a deep learning method to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME.
  arXiv  Detail & Related papers  (2022-03-27T21:06:08Z)
• Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction [92.16318571149553]
  We propose a multiscale-temporal graph neural network (MST-GNN) to predict the future 3D-based skeleton human poses. The MST-GNN outperforms state-of-the-art methods in both short and long-term motion prediction.
  arXiv  Detail & Related papers  (2021-08-25T14:05:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.