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随机森林算法用于模拟河流流域的土壤纹理分类

Arthur Pereira Dos Santos¹, Alessandro Xavier da Silva Junior², Liliane Moreira Nery²

¹Department of Environmental Science, São Paulo State University (UNESP), Sorocaba, São Paulo, Brazil. arthur.p.santos@unesp.br.

Environmental monitoring and assessment

|February 26, 2025

概括

机器学习可以准确预测土壤质地,这对农业和环境至关重要. 这项研究使用了索罗卡布库河流域的随机森林,实现了高精度和支持可持续的土地管理.

科学领域:

农业科学农业科学
环境科学环境科学
地质科学地质科学

背景情况:

土壤质地由沙子,泥和粘土的比例来定义,对农业和生态功能至关重要.
传统的土壤质地分类方法昂贵且耗时,限制了广泛应用.
机器学习为准确地预测土壤质地提供了一种具有成本效益和效率的替代方案.

研究的目的:

整合地质处理,精准农业和机器学习,以准确地分类土壤质地.
评估Sorocabuçu河流盆地 (SRB) 土壤质地随机森林算法的预测性能.
为农业地区的可持续土地管理和粮食安全提供基础.

主要方法:

在SRB中根据地形和土地利用选择了27个采样点.
使用管管法进行颗粒度分析,用于分离土壤成分.
采用随机森林算法进行土壤质地分类,并通过GIS进行空间插值.

主要成果:

随机森林模型实现了高准确度 (0.92整体准确度,0.88卡帕指数) 与低出袋误差 (2.78%).
确定了粘土和高砂/砂水平的多样空间分布,表明没有保护实践的潜在侵蚀风险.
由于其中间性质,观察到沙粘土 (SCL) 类的分类挑战.

结论:

综合方法证明了土壤质地分类的优秀预测能力.
这些发现支持对土壤结构的更好理解,以改善SRB的农业和环境可持续性.
该方法可适应其他地区和农业环境,在同质的土壤区域有改进的潜力.

关键词:

机器学习机器学习精准农业精准农业精准农业土壤侵蚀导致土壤侵蚀.土壤质地分类土壤质地分类

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