通过整体机器学习模拟土壤盐度动态,棉花产量和滴灌下的蒸发透气
Zewei Jiang1, Shihong Yang1,2,3, Shide Dong4,5
1College of Agricultural Science and Engineering, Hohai University, Nanjing, China.
Frontiers in plant science
|June 23, 2023
概括
机器学习模型准确地预测了滴灌灌下棉花的土壤盐度和作物蒸发. 整体机器学习方法的表现优于单个模型,有助于干旱,盐地区的水资源管理.
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:
- 土壤盐分化威胁到棉花生产,需要干旱地区高效的水资源管理.
- 滴灌有助于提高水和肥料的使用,但需要准确地预测土壤盐度和蒸发透气.
- 传统的水文模型是复杂的,限制了实际应用.
研究的目的:
- 开发一种机器学习 (ML) 方法来模拟土壤盐度,蒸发透气 (ET) 和棉花产量.
- 与单个模型相比,评估集合ML模型的性能.
- 确定影响预测准确性的关键输入因素.
主要方法:
- 从134个文献来源编制了一个全球数据集.
- 开发了堆叠组合ML模型,并与梯度增强决策树 (GBDT),随机森林 (RF) 和极端梯度增强回归 (XGBR) 进行了比较.
- 模型输入包括土壤数据,气象因素,灌数据和其他相关变量.
主要成果:
- ML模型在预测土壤盐度,ET和棉花产量方面取得了高准确性 (R = 0.78-0.99).
- 集体ML模型显示出优异的性能,比单个模型增加了0.02%-19.31%的R.
- 土壤深度,滴水器的距离和播种后的几天是影响模型准确性的重要因素.
结论:
- 集团ML为预测棉花土壤盐度和ET的传统模型提供了强大而准确的替代方案.
- 开发的模型可以指导干旱和盐水环境中的灌时间表.
- 进一步的研究应该专注于优化输入组合,以提高预测能力.
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