使用机器学习方法建模土壤水动力学和水凝剂量优化:在不同散装密度下对砂泥土进行案例研究
José Wilson de Oliveira Magalhães1, Ednaldo José Ferreira2, Líllian Alexia Lameira da Rocha1
1College of Agricultural Science, Department of Agricultural Engineering, São Paulo State University, Av. Universitária 3780, 18610-307 Botucatu, SP, Brazil.
ACS omega
|January 8, 2026
概括
可生物降解的水凝显著提高了沙粘土土的土壤水,帮助农业在气候变化中. 一种新的机器学习方法快速估计有效的水凝剂量,以实现可持续的水资源管理.
科学领域:
- 农业科学 农业科学
- 土壤科学 土壤科学
- 聚合物科学 聚合物科学
背景情况:
- 气候变化加剧了巴西的干旱,通过降雨和温度的变化影响了农业.
- 可生物降解的性聚合物 (水凝) 提供了一种潜在的解决方案,以减轻土壤的缺水.
- 土壤水凝水的动态是复杂的,受土壤类型,密度和剂量的影响,其余效应是显著的.
研究的目的:
- 为了评估水凝在各种密度的沙粘土泥土中保持水分的有效性.
- 开发一种基于机器学习的快速分析方法,用于估计活性凝剂量.
- 为了解决散装密度对水凝剂量动态的影响.
主要方法:
- 在不同的散装密度下,在沙粘土土土壤中评估水凝剂量.
- 开发了一个机器学习模型 (局部加权回归) 使用短土壤含水量时间序列.
- 通过交叉验证验证模型以评估准确性和相关性.
主要成果:
- 一种3克L-1的水凝剂量显著增强了土壤的保留水.
- 机器学习模型在交叉验证中实现了高相关性 (0.875) 和低误差 (0.749 g L-1).
- 开发的方法准确地估计了水凝剂量,而不需要土壤密度信息.
结论:
- 水凝,特别是3克L-1,可以有效地改善沙粘土土壤中的水留.
- 机器学习方法为农业应用中估计水凝剂量提供了一个快速而实用的工具.
- 这些发现支持水凝的高效和可持续使用,以改善农业的水资源管理,从而实现现场规模的干预.
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