通过机器学习在干旱地区缓解作物建模的不确定性
1Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. m-nouri@areeo.ac.ir.
Scientific reports
|November 29, 2025
概括
这项研究通过机器学习提高了干旱土地农业气候数据的可靠性. 先进的方法提高了作物模型的准确性,支持面临极端气候的脆弱农业系统的粮食安全.
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:
- 干旱地区的干旱农业系统非常容易受到气候极端的影响,导致产量显著变化,威胁到粮食安全.
- 可靠的作物建模对于管理这些系统至关重要,特别是在数据稀缺的地区.
研究的目的:
- 评估和改进CSM-CERES-小麦作物模型在伊朗干旱地区的性能,使用各种气象数据集.
- 开发和应用一种基于机器学习 (ML) 的新型框架,用于不偏见和整合气候数据,以提高作物建模的准确性.
主要方法:
- 使用了五个格式气象数据集 (CFS,ERA5-Land,CHIRPS,IMERG,PERSIANN-CDR) 来计算降水,温度和太阳辐射.
- 应用了一个集群-无偏向-组装框架,使用了四个ML算法,包括光梯度增强机和随机森林.
- 实施了一个 TOTAL 场景,结合所有纠正的变量和使用引导验证的结果.
主要成果:
- 光梯度增强机和随机森林显著改善了降水和最低温度数据的准确性 (Nash-Sutcliffe效率从0.11-0.17增加到0.47的降水,和0.56-0.88到0.89-0.96的温度/太阳辐射).
- 基于ML的TOTAL场景,纠正所有变量,在大约60%的案例中增强了产量和水应力模拟.
- 基于ML的无偏差组合性能优于传统方法,证明了可靠性和可转移性.
结论:
- 基于ML的先进无偏置组合框架对于提高作物建模中的气候数据可靠性是有价值的.
- 这些方法提供了实际的解决方案,以支持面临气候极端和数据短缺的干旱土地农业系统.
- 这些发现为通过改进气候数据利用来加强脆弱地区的粮食安全提供了指导.
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