在半干旱气候条件下的输入数据限制场景下,评估数据智能算法在建模每日参考蒸发转化时的数据智能算法
Jitendra Rajput1, Man Singh1, K Lal1
1Water Technology Center, ICAR-IARI, New Delhi 110012, India
概括
准确的作物蒸发转化 (ETo) 建模对于灌至关重要. XGBoost 集合树和同位素回归算法显示出在半干旱地区预测每日ETo的前景,优于其他机器学习模型.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 农作物蒸发转化 (ETo) 是有效的灌系统设计和水资源管理的关键因素.
- 准确的ETo估计对于优化农业用水,特别是在半干旱地区至关重要.
研究的目的:
- 评估四个机器学习算法的性能 (XGBoost线性,XGBoost集合树,多项式回归,同位素回归) 用于建模每日参考蒸发转化 (ETo).
- 使用新德里的IARI数据,在完全和有限数据集场景下比较这些模型.
- 为了确定最有效的算法精确的ETo预测在半干旱的气候.
主要方法:
- 开发和训练了四种不同的机器学习模型:XGBoost线性,XGBoost集合树,多项式回归和同位素回归.
- 使用完整和有限的数据集评估模型性能,模拟不同的输入变量场景 (例如,Tmax,Tmin).
- 与已建立的Penman-Monteith (PM56) 模型对每日ETo估计的验证模型有效性.
主要成果:
- 在模型训练阶段,在完全和有限数据条件下,XGBoost Ensemble Tree在模型训练阶段表现出卓越的性能.
- 在测试阶段,同位素回归模型为S1 (Tmax) 和S2 (Tmax,Tmin) 场景提供了更好的结果.
- 在各种输入数据场景中,XGBoost Ensemble Tree始终优于其他模型,实现了最佳的统计指标 (r,MAE,MSE,RMSE,MAPE).
结论:
- 由于其强大的性能,建议使用XGBoost Ensemble Tree算法在半干旱环境中准确的每日ETo建模.
- 机器学习方法,特别是XGBoost Ensemble Tree,为ETo估计提供了传统方法的可行和精确的替代方案.
- 最好的模型的选择可能取决于特定的数据集限制和可用于ETo预测的输入变量.
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