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| DOI | 10.1016/j.jclepro.2024.141035 |
| Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development | |
| 发表日期 | 2024 |
| ISSN | 0959-6526 |
| EISSN | 1879-1786 |
| 起始页码 | 444 |
| 卷号 | 444 |
| 英文摘要 | Accurate prediction of Land Surface Temperature (LST) is critical for understanding and mitigating the effects of climate change and land use dynamics. This study proposes a novel approach that leverages ensemble models and correlation analysis based on Landsat -8 satellite data to forecast LST and explore its environmental relationships. Time -series satellite data spanning winter and summer seasons of 2018-2019 was retrieved from the Google Earth Engine (GEE) platform. LST, normalized difference vegetation index (NDVI), rainfall, and evapotranspiration (ET) datasets were derived from Landsat -8 data within GEE to facilitate LST modeling. The ensemble framework combines three powerful machine learning algorithms: XG-Boost, Bagging-XG-Boost, and AdaBoost, to enhance the accuracy and robustness of LST predictions. Compared to standalone models, the proposed ensemble models demonstrated significant improvements in LST prediction accuracy. While XG-Boost and AdaBoost achieved moderate accuracies with R2 values of 0.57 and 0.60, respectively, the Bagging ensemble model surpassed them with an outstanding R2 of 0.75. Furthermore, a correlation analysis by using linear regression (LR) model explored the relationships between ET, rainfall, NDVI, and LST. The analysis revealed strong positive correlations between NDVI and ET (R2 = 0.95), while correlations between NDVI and LST (R2 = 0.31) and NDVI and rainfall (R2 = 0.47) were weaker. These findings contribute significantly to our understanding of LST trends and the impact of climate change on environmental variables. Ultimately, this knowledge can inform effective sustainable decision-making in the area. |
| 英文关键词 | LST; Machine learning; Satellite data; GEE; Ensemble model; Energy; SDG |
| 语种 | 英语 |
| WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
| WOS类目 | Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences |
| WOS记录号 | WOS:001199069000001 |
| 来源期刊 | JOURNAL OF CLEANER PRODUCTION
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289485 |
| 作者单位 | Universiti Tenaga Nasional; Al-Ayen University; King Saud University; University of Bucharest; Transylvania University of Brasov; Lanzhou University; College of Technology & Engineering Udaipur; University of Burdwan |
| 推荐引用方式 GB/T 7714 | . Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development[J],2024,444. |
| APA | (2024).Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development.JOURNAL OF CLEANER PRODUCTION,444. |
| MLA | "Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development".JOURNAL OF CLEANER PRODUCTION 444(2024). |
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