机器学习方法用于对参考蒸发转化 (ETO) 的增强估计:一个比较性评估
1Department of Agricultural and Biosystems Engineering, Faculty of Agriculture, Benha University, Banha, Egypt. abousrie.ahmad@fagr.bu.edu.eg.
Scientific reports
|November 4, 2025
概括
准确的参考蒸发转化率 (ETO) 估计对于水资源管理至关重要. 随机森林模型为数据稀缺地区提供了强大的解决方案,优于其他方法.
科学领域:
- 水文学的水文学
- 农业科学 农业科学
- 机器学习 机器学习
背景情况:
- 准确的参考蒸发透气 (ETo) 估计对于水资源管理至关重要,特别是在数据有限的地区.
- 现有的模型通常需要大量的数据或复杂的方法,这阻碍了它们在资源有限的环境中使用.
研究的目的:
- 评估静态机器学习模型 (KNN,DT,RF) 用于使用各种输入场景进行每日ETO估计.
- 确定最有效的模型和主要气象预测指标,用于在数据稀缺的地区进行ETO估计.
主要方法:
- 应用K-最近邻居 (KNN),决策树 (DT) 和随机森林 (RF) 模型.
- 时间依赖性分析以证明静态模型的应用.
- 使用全功能和最小单变量输入数据集进行ETO估计.
主要成果:
- 随机森林 (RF) 展示了卓越的性能,实现0.52mm/day的根平均平方误差 (RMSE) 和0.96.9的R2.
- 温度和太阳辐射被确定为ETO估计的最重要的预测因素.
- 射频模型在不同的输入数据场景中显示出稳定性.
结论:
- 静态机器学习模型,特别是RF,为数据稀缺地区的ETO估计提供了实用和高效的方法.
- 在有限的数据条件下,这些发现为加强水资源管理和农业规划提供了可靠的工具.
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