气象变量和机器学习算法对韩国大米产量预测的影响
Subin Ha1, Yong-Tak Kim1, Eun-Soon Im2,3
1Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China.
International journal of biometeorology
|September 4, 2023
概括
这项研究表明,高分辨率的天气数据显著改善了作物产量预测. 机器学习模型,特别是长短期记忆 (LSTM),在使用每周的气候数据来估计大米产量时表现最好.
科学领域:
- 农业科学 农业科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 农作物生产率对天气模式非常敏感,这推动了利用气象数据预测产量的努力.
- 机器学习已经推进了作物产量估计,但大多数模型依赖于观测数据,对气候模型输出的使用有限.
研究的目的:
- 使用ERA5再分析 (ERA-O) 和动态缩小 (ERA-DS) 的气象数据估计韩国大米产量.
- 用不同的数据分辨率和源来比较支持矢量机 (SVM) 和长短期内存 (LSTM) 模型的性能.
- 评估气象数据分辨率 (每周或每月) 对产量预测准确性的影响.
主要方法:
- 经验证的ERA5再分析 (ERA-O) 和动态缩小 (ERA-DS) 数据与观测数据 (OBS) 相比.
- 训练SVM和LSTM模型使用八个气象变量从OBS,ERA-O和ERA-DS每周和每月间隔.
- 基于收益率估计准确性的评估模型性能.
主要成果:
- 对于SVM和LSTM模型来说,每周的气象数据集总是比每月的数据集提供更好的作物估计.
- 总体而言,LSTM模型的表现优于SVM模型,尤其是在使用每周ERA-DS数据进行训练时.
- 使用LSTM模型训练了所有八个气象变量的每周数据,实现了收益率估计的最高准确性.
结论:
- 气象输入数据的高空间和时间分辨率对于准确的作物产量预测至关重要.
- 气候模型数据的动态缩小为改善产量估计模型提供了显著的附加值.
- LSTM模型在作物产量预测方面表现出卓越的表现,特别是在高分辨率,缩小规模的气候数据方面.
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