机器学习模型的评估,用于估计半干旱地区的每日参考蒸发转化率
Mohammed A Atiea1, Doaa E El-Agha2
1Faculty of Computers and Information, Suez University, P.O. Box: 43221, Suez, Egypt.
概括
机器学习模型使用有限的天气数据准确估计每日参考蒸发转化率 (ET0). 风速和最高温度是埃及水资源管理的关键预测指标.
科学领域:
- 农业科学 农业科学
- 水文学的水文学
- 数据科学数据科学数据科学
背景情况:
- 蒸发透气 (ET) 对于水资源管理至关重要,特别是在干旱的农业地区.
- 准确估计每日参考蒸发转化率 (ET0) 对于优化灌和用水效率至关重要.
- 在新开发的农业地区,有限的气象数据给可靠的ET0预测带来了挑战.
研究的目的:
- 评估各种机器学习 (ML) 模型的性能,以利用埃及有限的气象数据估计每日ET0.
- 为了确定最佳的输入特征和ML模型,以准确预测ET0.
- 为改善干旱和半干旱地区的水资源管理提供实用方法.
主要方法:
- 在埃及的14个站点中利用了1943-2024年的每日气象数据.
- 对比了20个ML模型,包括分类提升 (CatBoost),轻梯度提升机 (LightGBM) 和梯度提升机 (GB).
- 采用了五种特征选择方法,包括顺序前选择 (SFS),以确定关键的气象变量.
主要成果:
- CatBoost回归器表现出卓越的性能,即使只有三个输入功能.
- 当使用由SFS选择的两个功能时,CatBoost,LightGBM和GB模型显示出一致的高性能.
- 风速 (U) 和最大温度 (Tmax) 被确定为埃及每日ET0的最关键预测指标.
结论:
- 机器学习模型,特别是CatBoost,提供了一个可靠的解决方案,以有限的数据来估计ET0.
- 有效的特征选择方法提高了用于ET0估计的ML模型的预测能力.
- 这些发现支持在埃及新回收的沙漠地区和全球类似气候中改进水资源管理策略.
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