季节和时间组合模型用于准确的近地表空气温度估计
1Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Republic of Korea.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究通过将季节和时间数据与陆地表面温度 (LST) 卫星数据集成来增强近地表空气温度 (NSAT) 估计. 新型组合模型显著提高了环境和城市热监测的准确性.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 接近地表的空气温度 (NSAT) 对于了解热和城市环境至关重要.
- 传统的NSAT估计方法往往忽视季节和时间变化,限制了准确性.
- 来自卫星的陆地表面温度 (LST) 是温度估计的关键数据来源.
研究的目的:
- 为增强NSAT估计开发一种新型组合模型.
- 将季节和时间信息与LST数据结合起来.
- 通过特征重要性分析来确定影响NSAT的关键参数.
主要方法:
- 利用了2022年来自韩国147个气象站的数据.
- 使用随机森林回归来构建单独的季节性模型.
- 将季节性模型整合到最终组合模型中.
- 对参数选择进行特征重要性分析.
主要成果:
- 季节性模型的准确性各不相同,春季和秋季的R2更高.
- 整体模型实现了高性能:MAE为0.534,RMSE为0.391,R2为0.996.
- 整体模型的交叉验证R2为0.968.8.
- 功能重要性分析指导优化季节性参数选择.
结论:
- 结合季节和时间信息可以显著提高NSAT估计的准确性.
- 新型合奏方法在传统方法上提供了实质性的进步.
- 开发的模型支持精确的温度监测,预测和城市管理.
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