Climate Change Data Portal
| DOI | 10.1007/s00024-024-03472-6 |
| Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul | |
| 发表日期 | 2024 |
| ISSN | 0033-4553 |
| EISSN | 1420-9136 |
| 起始页码 | 181 |
| 结束页码 | 5 |
| 卷号 | 181期号:5 |
| 英文摘要 | This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly. |
| 英文关键词 | Surface solar radiation; decision tree; random forest; gradient boosted regression tree; machine learning; forecasting |
| 语种 | 英语 |
| WOS研究方向 | Geochemistry & Geophysics |
| WOS类目 | Geochemistry & Geophysics |
| WOS记录号 | WOS:001204773100003 |
| 来源期刊 | PURE AND APPLIED GEOPHYSICS
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/300165 |
| 作者单位 | Istanbul Technical University |
| 推荐引用方式 GB/T 7714 | . Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul[J],2024,181(5). |
| APA | (2024).Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul.PURE AND APPLIED GEOPHYSICS,181(5). |
| MLA | "Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul".PURE AND APPLIED GEOPHYSICS 181.5(2024). |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
