Precisely determining the thickness of soil, which is an essential parameter in environmental modelling, presents difficulties when applied to heterogenic large-scale areas. Current prediction models primarily concentrate on shallow soil depths and lack comprehensive spatial coverage. This study addresses this limitation by presenting the results of soil thickness assessment along three important roads in the Joshimath region (Indian Himalaya). Three different methods were examined incorporating geological and geomorphological data as input to obtain soil thickness maps: (1) a customized version of the conventional geomorphologically indexed soil thickness (GIST) model, modified specifically for the peculiarities of the research area, (2) the GIST model enhanced by Monte Carlo simulations (GIST-MCS), and (3) the random forest (RF) algorithm integrated with the GIST model (GIST-RF). By quantifying their errors and conducting validation using geophysical tests, the effectiveness of the models was assessed. Moreover, a critical comparison of the results provided useful insights to understand the peculiarities of the test site and how to adapt the site-specific customization of the models to the local features. The results indicate that the GIST model inadequately accounted for the substantial spatial variations in soil thickness observed across the study area. This is evident from the root-mean-square error (RMSE) of 5.28 m and the mean absolute error of 3.94 m. In contrast, the GIST-MCS model showed improvements, achieving an RMSE of 4.48 m and a mean absolute error of 2.86 m. However, the GIST-RF model demonstrated superior performance, yielding an RMSE of 2.40 m and a mean absolute error of 1.64 m. From a practical perspective, the generated soil thickness maps are particularly significant because they may serve as a crucial input parameter for further studies, including geotechnical assessments, environmental modelling, and decision-making processes.

中文翻译：

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Joshimath 地区土壤厚度测绘的经验方法和机器学习方法的比较研究（印度）

精确确定土壤厚度是环境建模中的一个重要参数，但在应用于异质大范围区域时会遇到困难。目前的预测模型主要集中在浅层土壤，缺乏全面的空间覆盖。本研究通过展示 Joshimath 地区（印度喜马拉雅山）三条重要道路沿线的土壤厚度评估结果来解决这一局限性。研究了三种不同的方法，将地质和地貌数据作为输入来获取土壤厚度图：（1）传统地貌指数土壤厚度（GIST）模型的定制版本，专门针对研究区域的特点进行了修改，（2）通过蒙特卡罗模拟增强的 GIST 模型（GIST-MCS），以及（3）与 GIST 模型集成的随机森林（RF）算法（GIST-RF）。通过量化误差并使用地球物理测试进行验证，评估了模型的有效性。此外，对结果的关键比较为了解测试站点的特殊性以及如何使模型的特定站点定制适应当地特征提供了有用的见解。结果表明，GIST 模型不足以解释整个研究区域观察到的土壤厚度的显着空间变化。从 5.28 m 的均方根误差 (RMSE) 和 3.94 m 的平均绝对误差可以明显看出这一点。相比之下，GIST-MCS 模型有所改进，RMSE 为 4.48 m，平均绝对误差为 2.86 m。然而，GIST-RF 模型表现出了卓越的性能，RMSE 为 2.40 m，平均绝对误差为 1.64 m。从实际角度来看，生成的土壤厚度图特别重要，因为它们可以作为进一步研究的关键输入参数，包括岩土工程评估、环境建模和决策过程。