使用基于埃及最佳子集回归模型的数据驱动技术进行空气温度估计和建模
Ahmed Elbeltagi1, Dinesh Kumar Vishwakarma2, Okan Mert Katipoğlu3
1Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt. ahmedelbeltagy81@mans.edu.eg.
Scientific reports
|June 20, 2025
概括
机器学习模型M5 pruned (M5P) 准确预测半干旱地区每日最低和最高气温. 这项研究确定M5P是改善水资源管理和农业规划的卓越模式.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 准确的空气温度预测对于水资源管理,水文研究和气候变化影响评估至关重要.
- 半干旱地区在供水方面面临着独特的挑战,因此精确的温度预测对于农业规划和可持续性至关重要.
研究的目的:
- 确定最准确的机器学习模型,用于预测半干旱环境中的每日最低 (Tmin) 和最高 (Tmax) 空气温度.
- 为了比较线性回归 (LR),增量回归 (AR),支向量机 (SVM),随机子空间 (RSS) 和M5修剪 (M5P) 模型的性能.
主要方法:
- 利用埃及加尔比亚省的历史每日温度数据 (1979-2014),分为75%的训练和25%的测试集.
- 应用最佳子集回归来确定Tmin和Tmax预测的最佳输入变量组合.
- 评估了五种基于根平均平方误差 (RMSE),平均绝对误差 (MAE),相对绝对误差 (RAE),纳什-萨特克利夫效率 (NSE) 和皮尔森相关系数 (PCC) 的机器学习模型.
主要成果:
- 与LR,AR,RSS和SVM相比,M5修剪 (M5P) 模型在预测Tmax和Tmin方面表现优异.
- M5P获得了最低的RMSE (2.4881°C为Tmin,2.7696°C为Tmax) 和MAE,以及最高的NSE (0.8048为Tmin,0.8720为Tmax).
- 统计测试 (弗里德曼ANOVA,Dunn的测试) 证实了模型之间的显著差异,M5P和RSS显示高变化和SVM显著不同.
结论:
- M5 pruned (M5P) 模型是一个强大的,准确的工具,用于在半干旱地区每天预测空气温度.
- 这些发现为提高水资源管理,灌和农业生产率的决策提供了宝贵的见解.
- 实施M5P可以提高运营效率,促进脆弱的半干旱环境中的可持续性.
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