Soil shear strength is a critical parameter in slope stability. Shear strength properties may vary significantly over large areas. Thus, the spatial estimates of shear strength properties are necessary for deterministic slope susceptibility mapping over large areas. However, measurements of shear strength parameters are often limited as compared to other soil properties such as Atterberg limit, bulk density and grain size distribution. Multivariate methods have been shown to improve prediction accuracy, but these methods have rarely been used to predict shear strength. In this study, attempts were made to evaluate the effectiveness of using the aforementioned soil properties in predicting the spatial variation of shear strength properties: effective cohesion (c’) and effective friction angle (ϕ’). The performance of ordinary kriging (OK), Random Forest (RF) and regression kriging (RK) in predicting c’ and ϕ’ of residual soils in Singapore were compared and evaluated. In addition, the sensitivity of the three methods to the sample size was investigated. The results of RF analysis revealed that the northing coordinate and percentage of fines were the most important variables for predicting ϕ’. The spatial coordinates and ϕ’ were also important variables for predicting c’. The predicted c’ and ϕ’ using RF and RK resulted in higher spatial heterogeneity than OK. Overall, RF had the smallest error as compared to OK and RK in predicting c’ and ϕ’ at all sample sizes, except for the prediction of ϕ’ using the largest sample size. This study also showed that RF and RK were more sensitive to sample size than OK. These results highlight the benefits of using auxiliary variables when mapping shear strength properties.

土壤抗剪强度是边坡稳定性的关键参数。剪切强度特性在大面积上可能会有很大变化。因此，抗剪强度特性的空间估计对于确定性大范围的坡度敏感性测绘是必要的。但是，与其他土壤特性（如阿特伯格极限，堆积密度和粒度分布）相比，抗剪强度参数的测量通常受到限制。已经显示了多元方法可以提高预测精度，但是这些方法很少用于预测剪切强度。在这项研究中，尝试评估使用上述土壤特性预测抗剪强度特性的空间变化的有效性：有效内聚力（c'）和有效摩擦角（ϕ'）。普通克里金的表现（OK），比较和评估了新加坡的剩余土壤c'和ϕ'的随机森林（RF）和回归克里格法（RK）。此外，还研究了三种方法对样本量的敏感性。射频分析的结果表明，北向坐标和罚款百分比是预测“ ϕ”的最重要变量。空间坐标和ϕ'也是预测c'的重要变量。使用RF和RK预测的c'和ϕ'比OK具有更高的空间异质性。总体而言，在所有样本量下预测c'和ϕ'时，与OK和RK相比，RF的误差最小，除了使用最大样本量进行ϕ'的预测外。这项研究还表明，RF和RK对样本大小的敏感性高于OK。