Thermal conductivity is one of the important properties required for understanding the frozen soils behavior. There are several models available in the literature for the prediction of thermal conductivity of frozen soils based on the proportions of unfrozen water, ice, gas, and soil particles. In this study, two ensemble learning methods-based models; namely, the Random Forest (RF) model and the Least Squares Boosting (LSB) model, are extended to estimate the thermal conductivity of frozen soils. These models utilize basic soil properties as input parameters that include water content, dry density, temperature, and fractions of gravel, sand, silt, and clay, can be measured easily, or determined. Additionally, seven widely used thermal conductivity models, referred to as the traditional models for frozen soils, were evaluated. Both the RF and LSB models, as well as the traditional models, were assessed using data of 823 tests derived from 43 soils with different textures that were gathered from the literature. The results highlight that the traditional models have their strengths and limitations in terms of their use for different types of soils. In contrast, the proposed ensemble learning methods-based models provide higher prediction accuracy compared to the traditional models and can be applied to all soil types and temperature ranges. Furthermore, estimation from the ensemble learning methods-based models can be used to provide probability of multi-dimensional analysis of frozen soils.

中文翻译：

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使用集成学习方法根据基本土壤特性预测冻土的热导率


导热系数是了解冻土行为所需的重要特性之一。文献中提供了多种模型，用于根据未冻结的水、冰、气体和土壤颗粒的比例来预测冻土的热导率。在这项研究中，两种基于集成学习方法的模型;即随机森林 （RF） 模型和最小二乘提升 （LSB） 模型被扩展为估计冻土的热导率。这些模型利用基本的土壤特性作为输入参数，包括含水量、干密度、温度以及砾石、沙子、淤泥和粘土的分数，可以很容易地测量或确定。此外，还评估了 7 个广泛使用的导热模型，称为冻土的传统模型。RF 和 LSB 模型以及传统模型都使用从文献中收集的 43 种具有不同质地的土壤的 823 次测试数据进行评估。结果突出表明，传统模型在用于不同类型土壤方面有其优势和局限性。相比之下，与传统模型相比，所提出的基于集成学习方法的模型提供了更高的预测精度，并且可以应用于所有土壤类型和温度范围。此外，基于集成学习方法的模型的估计可用于提供冻土多维分析的概率。