Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction

结合物理和基于机器学习的模型进行全能量范围太阳能高能粒子事件预测

• 批准号: 2204363
• 负责人: Soukaina Filali Boubrahimi
• 金额: $ 52.71万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204363&HistoricalAwards=false
• 关键词: Combining; Physics; Machine; Learning; based

Solar flares and coronal mass ejections (CMEs) accelerate solar energetic particles (SEPs) into the heliosphere. When SEPs are directed towards Earth, they cause impacts for human technology. They can cause satellite damage, high dosages of harmful radiation for astronauts, and many other effects. This project develops a new SEP prediction model, led by an early-career female PI. Two graduate students will be supported. This project includes an outreach plan to impact students at all educational levels in Utah from middle school to college level through lecture series talks in Utah State University Library, college-level presentations, and solar crafts activities during summer camp. The PI also will organize a public Kaggle competition to promote scientific research on SEP events prediction. The PI will prepare a monthly “Space Weather” five-lectures series that will take place at the USU library. Additionally, the research results will be taught as part of the data mining-related classes curriculum across many Utah State University regional campuses, which includes campuses located in rural areas of Utah. Inspired by the success of machine learning (ML) in several areas of physical and life sciences, the project will implement new advanced methods to augment current datasets, improve SEP forecasts, and answer essential scientific questions. To achieve this vision, the new model will identify SEP predictive features based on the wealth of existing spatiotemporal and physical metadata of SEP parent events. These features will be extracted from image parameters and trajectory metadata of active regions and solar prominences preceding CMEs and solar flares parents’ events. The newly engineered features will improve our understanding of the underlying processes leading to particles acceleration following fast CMEs and solar flares events (Thrust 1). The second challenge that the project will address is the small amount of SEP events that total merely 349 occurrences across all the energy ranges to this day. The project will augment the current SEP events catalog and time series dataset by generating new realistic synthetic solar events (Thrust 2). Finally, the project will build an accurate and robust ensemble model that combines state-of-the-art physics-based models, machine learning-based models, and new spatiotemporal methods for full-energy-range SEP events prediction (Thrust 3). One of the by-products of this research will be a one-of-its-own kind augmented SEP catalog supplemented with the new spatiotemporal metadata that will be freely available through Application Programming Interface (API) for its potential usage e.g., to train machine-learning-based and physics- based models. The final operational ensemble model will be deployed at the Community Coordinated Modeling Center (CCMC), supported by NSF and NASA, and deposited in publicly available repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

太阳耀斑和日冕物质抛射（CME）加速太阳高能粒子（SEP）进入日光层。当SEP被导向地球时，它们会对人类技术造成影响。它们会造成卫星损坏，对宇航员造成高剂量的有害辐射，以及许多其他影响。本研究开发了一个新的SEP预测模型，由一位职业生涯早期的女性PI领导。将资助两名研究生。该项目包括一个外展计划，通过在犹他州州立大学图书馆的系列讲座、大学水平的演讲和夏令营期间的太阳能工艺品活动，影响犹他州从中学到大学的所有教育水平的学生。PI还将组织一场公开的Kaggle竞赛，以促进SEP事件预测的科学研究。PI将准备每月一次的“空间天气”五个系列讲座，将在USU图书馆举行。此外，研究结果将作为数据挖掘相关课程的一部分在许多犹他州州立大学区域校区教授，其中包括位于犹他州农村地区的校区。受机器学习（ML）在物理和生命科学多个领域的成功启发，该项目将实施新的先进方法来增强当前数据集，改进SEP预测，并回答基本的科学问题。为了实现这一愿景，新模型将根据SEP父事件的现有时空和物理元数据来识别SEP预测功能。这些特征将从CME和太阳耀斑母事件之前的活动区域和太阳活动的图像参数和轨迹元数据中提取。新设计的功能将提高我们对快速CME和太阳耀斑事件（推力1）后导致粒子加速的潜在过程的理解。该项目将解决的第二个挑战是，迄今为止，在所有能量范围内发生的SEP事件数量很少，总共只有349次。该项目将通过生成新的真实合成太阳事件（推力2）来增加当前SEP事件目录和时间序列数据集。最后，该项目将建立一个准确和强大的集成模型，该模型结合了最先进的基于物理的模型，基于机器学习的模型和新的时空方法，用于全能量范围SEP事件预测（推力3）。这项研究的副产品之一将是一个独一无二的增强SEP目录，并补充了新的时空元数据，该元数据将通过应用程序编程接口（API）免费提供，用于其潜在用途，例如，来训练基于机器学习和物理学的模型。最终的操作集合模型将部署在社区协调建模中心（CCMC），由NSF和NASA支持，并存放在公共可用的repositors.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

ML-Based Streamflow Prediction in the Upper Colorado River Basin Using Climate Variables Time Series Data

使用气候变量时间序列数据对科罗拉多河流域上游基于机器学习的水流进行预测

• DOI: 10.3390/hydrology10020029
• 发表时间: 2023
• 期刊: Hydrology
• 影响因子: 3.2
• 作者: Hosseinzadeh, Pouya;Nassar, Ayman;Boubrahimi, Soukaina Filali;Hamdi, Shah Muhammad
• 通讯作者: Hamdi, Shah Muhammad