{"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}
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.
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.