The quantification of soil strength parameters is a crucial prerequisite for constructing physical models related to hydro‐geophysical processes. However, due to ignoring soil spatial variability at different scales, traditional parameter assignment strategies, such as assigning values depending on land use classification or other classification systems, as well as those extrapolation and interpolation methods, are insufficient for physical process modelling. This work addressed this deficiency by proposing a method to derive stochastic soil strength parameters under hybrid machine learning (ML) models, taking into account the grain‐size distribution (GSD) of soil with scaling invariance. The nonlinear connection between GSD parameters (derived from GSD curves, such as μ and Dc), moisture content, and soil shear strength parameters was fitted by the suggested hybrid ML model. An analysis of a case study revealed that: (i) the Multi‐layer Perceptron optimized by the African Vulture Optimization Algorithm (AVOA) algorithm performs the best and can estimate the shear strength parameters of soil mass on vegetated slopes; (ii) all the selected ML models showed significant improvements in predictive performance after optimization with the AVOA, with R2 scores increasing by 24.72% for Support Vector Regressor, 34.04% for eXtreme Gradient Boosting, and 35.53% for Multi‐layer Perceptron; and (iii) soil cohesion has an increasing relationship with the GSD parameter μ, while soil internal friction angle has a negative correlation with the grain‐size parameter Dc. The proposed methodology can give predictions of soil shear strength distribution parameters, providing parameter support for the physical modelling of surface process dynamics.

中文翻译：

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一种在混合机器学习模型下推导随机土壤强度参数的 GSD 驱动方法


土壤强度参数的量化是构建与水文地球物理过程相关的物理模型的关键前提。然而，由于忽略了不同尺度上的土壤空间变化，传统的参数分配策略，例如根据土地利用分类或其他分类系统分配值，以及那些外推和插值方法，对于物理过程建模来说是不够的。这项工作通过提出一种在混合机器学习 （ML） 模型下推导随机土壤强度参数的方法来解决这一缺陷，同时考虑到具有缩放不变性的土壤的粒度分布 （GSD）。GSD 参数（源自 GSD 曲线，如 μ 和 Dc）、含水量和土壤剪切强度参数之间的非线性联系由建议的混合 ML 模型进行拟合。对案例研究的分析表明：（i） 由非洲秃鹰优化算法 （AVOA） 算法优化的多层感知器性能最好，可以估计植被斜坡上土体的剪切强度参数;（ii） 使用 AVOA 进行优化后，所有选定的 ML 模型在预测性能方面都显示出显着改善，支持向量回归器的 R2 分数提高了 24.72%，极限梯度提升的 R2 分数提高了 34.04%，多层感知器的 R2 分数提高了 35.53%;（iii） 土壤内聚力与 GSD 参数 μ 呈递增关系，而土壤内摩擦角与晶粒尺寸参数 DC 呈负相关。该方法可预测土体抗剪强度分布参数，为地表过程动力学的物理建模提供参数支持。