通过优化助推算法揭示宇宙射线和太阳活动对气候的影响

IF 1.8 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
{"title":"通过优化助推算法揭示宇宙射线和太阳活动对气候的影响","authors":"","doi":"10.1016/j.jastp.2024.106360","DOIUrl":null,"url":null,"abstract":"<div><div>This investigation explores the enhancement of climate anomaly predictions by incorporating Solar Sunspot Number (SSN) and Cosmic Ray (CR) data into climate models. Leveraging XGBoost and CatBoost regression methodologies enhanced by Atom Search Optimization (ASO) and Nuclear Reaction Optimization (NRO) for predictive analysis. Utilizing a dataset spanning from 1965 to 2020, comprising 672 data points per climate parameter, the study delves into the dynamics between CR flux, SSN variability, and climate parameters. The models aimed to forecast variations in total precipitation anomaly (TPA), total cloud cover anomaly (TCCA), and sea surface temperature anomaly (SSTA) based on decadal solar cycle activities and CR data. Our findings reveal the significant impact of integrating SSN and CR data into environmental prediction models for TCCA, TPA, and SSTA, employing CatBoost and XGBoost machine learning (ML) algorithms. Performance evaluation, centered on root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R<sup>2</sup>), and Nash-Sutcliffe efficiency (NSE), illuminated the efficacy of ASO and NRO in model optimization, particularly under scenarios with and without SSN/CR data inclusion. The analytical outcomes underscore the enhanced prediction accuracy for TCCA, TPA, and SSTA when incorporating SSN and CR data, with ASO generally outperforming NRO in optimizing model parameters. Our regression models, optimized using ASO and NRO, showed a marked improvement in SSTA forecasts, with an increase in the R<sup>2</sup> value from 0.73 to 0.76 when SSN/CR data were not included. The CatBoost was superior the XGBoost models with results of four error metrics. These results underscore the critical role of solar activity data and optimized algorithms in enhancing the accuracy and reliability of climate modeling. This study underscores the utility of advanced ML techniques and the importance of strategic variable selection in environmental modeling, offering new insights into the complex interactions between solar activity, CR, and climate dynamics.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling the impact of cosmic rays and solar activities on climate through optimized boost algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.jastp.2024.106360\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This investigation explores the enhancement of climate anomaly predictions by incorporating Solar Sunspot Number (SSN) and Cosmic Ray (CR) data into climate models. Leveraging XGBoost and CatBoost regression methodologies enhanced by Atom Search Optimization (ASO) and Nuclear Reaction Optimization (NRO) for predictive analysis. Utilizing a dataset spanning from 1965 to 2020, comprising 672 data points per climate parameter, the study delves into the dynamics between CR flux, SSN variability, and climate parameters. The models aimed to forecast variations in total precipitation anomaly (TPA), total cloud cover anomaly (TCCA), and sea surface temperature anomaly (SSTA) based on decadal solar cycle activities and CR data. Our findings reveal the significant impact of integrating SSN and CR data into environmental prediction models for TCCA, TPA, and SSTA, employing CatBoost and XGBoost machine learning (ML) algorithms. Performance evaluation, centered on root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R<sup>2</sup>), and Nash-Sutcliffe efficiency (NSE), illuminated the efficacy of ASO and NRO in model optimization, particularly under scenarios with and without SSN/CR data inclusion. The analytical outcomes underscore the enhanced prediction accuracy for TCCA, TPA, and SSTA when incorporating SSN and CR data, with ASO generally outperforming NRO in optimizing model parameters. Our regression models, optimized using ASO and NRO, showed a marked improvement in SSTA forecasts, with an increase in the R<sup>2</sup> value from 0.73 to 0.76 when SSN/CR data were not included. The CatBoost was superior the XGBoost models with results of four error metrics. These results underscore the critical role of solar activity data and optimized algorithms in enhancing the accuracy and reliability of climate modeling. This study underscores the utility of advanced ML techniques and the importance of strategic variable selection in environmental modeling, offering new insights into the complex interactions between solar activity, CR, and climate dynamics.</div></div>\",\"PeriodicalId\":15096,\"journal\":{\"name\":\"Journal of Atmospheric and Solar-Terrestrial Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Atmospheric and Solar-Terrestrial Physics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1364682624001883\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Solar-Terrestrial Physics","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364682624001883","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0

摘要

这项研究探讨了通过将太阳黑子数(SSN)和宇宙射线(CR)数据纳入气候模型来增强气候异常预测的问题。利用原子搜索优化(ASO)和核反应优化(NRO)增强的 XGBoost 和 CatBoost 回归方法进行预测分析。该研究利用从 1965 年到 2020 年的数据集,每个气候参数包含 672 个数据点,深入研究了 CR 通量、SSN 变率和气候参数之间的动态关系。模型旨在根据十年太阳周期活动和CR数据预测总降水异常(TPA)、总云量异常(TCCA)和海面温度异常(SSTA)的变化。我们的研究结果表明,采用 CatBoost 和 XGBoost 机器学习(ML)算法,将 SSN 和 CR 数据整合到 TCCA、TPA 和 SSTA 的环境预测模型中会产生重大影响。以均方根误差 (RMSE)、平均绝对误差 (MAE)、判定系数 (R2) 和纳什-苏特克利夫效率 (NSE) 为核心的性能评估表明了 ASO 和 NRO 在模型优化方面的功效,尤其是在包含和不包含 SSN/CR 数据的情况下。分析结果表明,在纳入 SSN 和 CR 数据时,TCCA、TPA 和 SSTA 的预测准确性得到了提高,ASO 在优化模型参数方面普遍优于 NRO。我们使用 ASO 和 NRO 对回归模型进行了优化,结果表明 SSTA 预测有明显改善,当不包含 SSN/CR 数据时,R2 值从 0.73 增加到 0.76。在四个误差指标上,CatBoost 模型优于 XGBoost 模型。这些结果强调了太阳活动数据和优化算法在提高气候建模的准确性和可靠性方面的关键作用。这项研究强调了先进的 ML 技术的实用性以及在环境建模中战略性变量选择的重要性,为太阳活动、CR 和气候动力学之间复杂的相互作用提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling the impact of cosmic rays and solar activities on climate through optimized boost algorithms
This investigation explores the enhancement of climate anomaly predictions by incorporating Solar Sunspot Number (SSN) and Cosmic Ray (CR) data into climate models. Leveraging XGBoost and CatBoost regression methodologies enhanced by Atom Search Optimization (ASO) and Nuclear Reaction Optimization (NRO) for predictive analysis. Utilizing a dataset spanning from 1965 to 2020, comprising 672 data points per climate parameter, the study delves into the dynamics between CR flux, SSN variability, and climate parameters. The models aimed to forecast variations in total precipitation anomaly (TPA), total cloud cover anomaly (TCCA), and sea surface temperature anomaly (SSTA) based on decadal solar cycle activities and CR data. Our findings reveal the significant impact of integrating SSN and CR data into environmental prediction models for TCCA, TPA, and SSTA, employing CatBoost and XGBoost machine learning (ML) algorithms. Performance evaluation, centered on root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE), illuminated the efficacy of ASO and NRO in model optimization, particularly under scenarios with and without SSN/CR data inclusion. The analytical outcomes underscore the enhanced prediction accuracy for TCCA, TPA, and SSTA when incorporating SSN and CR data, with ASO generally outperforming NRO in optimizing model parameters. Our regression models, optimized using ASO and NRO, showed a marked improvement in SSTA forecasts, with an increase in the R2 value from 0.73 to 0.76 when SSN/CR data were not included. The CatBoost was superior the XGBoost models with results of four error metrics. These results underscore the critical role of solar activity data and optimized algorithms in enhancing the accuracy and reliability of climate modeling. This study underscores the utility of advanced ML techniques and the importance of strategic variable selection in environmental modeling, offering new insights into the complex interactions between solar activity, CR, and climate dynamics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Atmospheric and Solar-Terrestrial Physics
Journal of Atmospheric and Solar-Terrestrial Physics 地学-地球化学与地球物理
CiteScore
4.10
自引率
5.30%
发文量
95
审稿时长
6 months
期刊介绍: The Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is an international journal concerned with the inter-disciplinary science of the Earth''s atmospheric and space environment, especially the highly varied and highly variable physical phenomena that occur in this natural laboratory and the processes that couple them. The journal covers the physical processes operating in the troposphere, stratosphere, mesosphere, thermosphere, ionosphere, magnetosphere, the Sun, interplanetary medium, and heliosphere. Phenomena occurring in other "spheres", solar influences on climate, and supporting laboratory measurements are also considered. The journal deals especially with the coupling between the different regions. Solar flares, coronal mass ejections, and other energetic events on the Sun create interesting and important perturbations in the near-Earth space environment. The physics of such "space weather" is central to the Journal of Atmospheric and Solar-Terrestrial Physics and the journal welcomes papers that lead in the direction of a predictive understanding of the coupled system. Regarding the upper atmosphere, the subjects of aeronomy, geomagnetism and geoelectricity, auroral phenomena, radio wave propagation, and plasma instabilities, are examples within the broad field of solar-terrestrial physics which emphasise the energy exchange between the solar wind, the magnetospheric and ionospheric plasmas, and the neutral gas. In the lower atmosphere, topics covered range from mesoscale to global scale dynamics, to atmospheric electricity, lightning and its effects, and to anthropogenic changes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信