在沿海地区使用改进的浅层和深度学习模型预测每日参考蒸发透气的创新方法:一项比较研究
Hussam Eldin Elzain1, Osman A Abdalla2, Mohammed Abdallah3
1Water Research Center, Sultan Qaboos University, P.O. 50, AlKhoud 123, Oman.
Journal of environmental management
|February 15, 2024
概括
准确的参考蒸发转化率 (ETO) 估计至关重要. 带有伪标签 (CBR-PL) 的Catboost回归器在日常ETO预测中表现优于深度学习模型,特别是在有限的气候数据下.
科学领域:
- 水文学的水文学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 准确的参考蒸发透气 (ETo) 估计对于水资源管理,水文学和农业至关重要.
- 粮农组织 - 56 潘曼 - 蒙蒂斯 (FAO-56PM) 是标准,但需要大量的数据,通常在全球范围内无法获得.
- 浅层和深度学习模型为使用有限数据的ETO估计提供了替代方案.
研究的目的:
- 为了比较浅层学习 (SL) 和深度学习 (DL) 模型的每日ETO估计与FAO-56PM标准.
- 评估半监督伪标签 (PL) 技术在提高模型性能方面的有效性.
- 确定在数据稀缺的沿海地区用于ETO预测的最佳气象变量组合.
主要方法:
- 使用 Catboost Regressor (CBR) 作为新型 SL 模型和 1D-CNN,LSTM,GRU 作为 DL 模型.
- 在所有测试模型中集成了一种半监督伪标签 (PL) 技术.
- 开发了六种场景,使用各种气象变量组合 (温度,湿度,风速等). ) 的情况.
主要成果:
- 在所有场景中,PL技术有效地减少了模型训练期间的系统错误.
- 包括Tmin,Tmax,Tmean和RH在内的输入组合为所有模型提供了卓越的性能.
- CBR-PL模型展示了最好的概括性,准确性和稳定性,计算成本低于DL模型.
结论:
- CBR-PL模型是预测每日ETO的高效和推工具,特别是在数据有限的沿海环境中.
- 用PL增强的浅层学习模型可以成为对ETO估计的复杂深度学习方法的可行和高效替代方案.
- 包括温度和相对湿度在内的气象数据是准确的ETO建模的关键预测因素.
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