相关实验视频
多输出深度学习用于科威特空气和地表温度的高频预测
Shehroz S Khan1, Rami Al-Hajj2
1College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait. shehroz.khan@aum.edu.kw.
Scientific reports
|November 7, 2025
概括
在干旱地区,准确的空气和表面温度预测至关重要. 像FTTransformer和LSTM这样的深度学习模型显示出高性能,优于5分钟气候预测的传统方法.
科学领域:
- 环境科学 环境科学
- 气候建模气候模型
- 数据科学数据科学数据科学
背景情况:
- 准确的空气和表面温度预测对于城市规划和气候弹性至关重要,特别是在干旱地区.
- 高频气候数据对于理解和预测局部温度变化至关重要.
研究的目的:
- 评估使用高频气候数据预测空气和表面温度的多输出回归模型.
- 为了对科威特的5分钟温度预测进行深度学习和传统机器学习方法的比较.
主要方法:
- 在科威特使用了30个环境变量,在四年内每5分钟收集一次.
- 基准测试了10个模型,包括FTTransformer和LSTM,使用1年休假的验证策略.
- 在模型解释中使用SHAP和顺序的重要性,以确定关键预测因素.
主要成果:
- 在5分钟的分辨率下,FTTransformer和LSTM实现了6个温度变量的高预测性能 (R2=0.998,MSE=0.13,MAE=0.24).
- 深度学习模型显著超过了传统的机器学习模型.
- 多年来,FTTransformer表现出稳定的准确性,而LSTM的性能因异常数据而下降.
结论:
- 深度学习模型,特别是FTTransformer,为干旱地区的高频气候预测提供了强大而准确的解决方案.
- 关键的环境变量显著影响温度预测的准确性.
- 与LSTM相比,FTTransformer在长期气候预测应用中显示出更高的稳定性.
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