Comparative analysis of machine learning techniques for accurate prediction of unfrozen water content in frozen soils

IF 3.8 2区 工程技术 Q1 ENGINEERING, CIVIL Cold Regions Science and Technology Pub Date : 2024-08-29 DOI:10.1016/j.coldregions.2024.104304
Jiaxian Li, Pengcheng Zhou, Yiqing Pu, Junping Ren, Fanyu Zhang, Chong Wang
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Abstract

Unfrozen water content (UWC) plays a critical role in determining the thermal, hydraulic, and mechanical properties of frozen soils. Existing empirical, semi-empirical, and theoretical models for UWC estimation have limitations in terms of accuracy as well as generalizability. To address these challenges, the present study explored the application of six machine learning techniques to predict UWC in frozen soils: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). Considering the UWC hysteresis phenomenon between the freezing and thawing processes, experimental UWC data collected from the literature were separated into two sub-datasets: freezing branch dataset (FBD) and thawing branch dataset (TBD). Based on that, a comprehensive framework integrating Bayesian optimization and 10-fold cross-validation was established to optimize the six models' hyperparameters and to evaluate their performance. The results highlighted significant variations in the predictive capability among the six machine learning models, with ensemble methods (i.e., RF, XGBoost, LightGBM) generally demonstrating superior accuracy. Feature importance analysis, robustness checks, and uncertainty quantification further elucidated the strengths and limitations of each model. The present study provides profound insights into the selection and application of machine learning models for accurately modeling the properties of frozen soils for cold regions science and engineering.

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准确预测冻土中未冻结含水量的机器学习技术比较分析
未冻水含量(UWC)在决定冻土的热力、水力和机械特性方面起着至关重要的作用。现有的用于估算 UWC 的经验、半经验和理论模型在准确性和普适性方面存在局限性。为了应对这些挑战,本研究探索了六种机器学习技术在冻土 UWC 预测中的应用:随机森林(RF)、极端梯度提升(XGBoost)、轻梯度提升机(LightGBM)、K-近邻(KNN)、支持向量回归(SVR)和反向传播神经网络(BPNN)。考虑到 UWC 在冻结和解冻过程之间的滞后现象,从文献中收集的 UWC 实验数据被分为两个子数据集:冻结树枝数据集(FBD)和解冻树枝数据集(TBD)。在此基础上,建立了贝叶斯优化和 10 倍交叉验证的综合框架,以优化六个模型的超参数并评估其性能。结果表明,六种机器学习模型的预测能力存在显著差异,而集合方法(即 RF、XGBoost、LightGBM)通常表现出更高的准确性。特征重要性分析、稳健性检查和不确定性量化进一步阐明了每个模型的优势和局限性。本研究为寒冷地区科学与工程领域选择和应用机器学习模型精确建模冻土特性提供了深刻的见解。
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来源期刊
Cold Regions Science and Technology
Cold Regions Science and Technology 工程技术-地球科学综合
CiteScore
7.40
自引率
12.20%
发文量
209
审稿时长
4.9 months
期刊介绍: Cold Regions Science and Technology is an international journal dealing with the science and technical problems of cold environments in both the polar regions and more temperate locations. It includes fundamental aspects of cryospheric sciences which have applications for cold regions problems as well as engineering topics which relate to the cryosphere. Emphasis is given to applied science with broad coverage of the physical and mechanical aspects of ice (including glaciers and sea ice), snow and snow avalanches, ice-water systems, ice-bonded soils and permafrost. Relevant aspects of Earth science, materials science, offshore and river ice engineering are also of primary interest. These include icing of ships and structures as well as trafficability in cold environments. Technological advances for cold regions in research, development, and engineering practice are relevant to the journal. Theoretical papers must include a detailed discussion of the potential application of the theory to address cold regions problems. The journal serves a wide range of specialists, providing a medium for interdisciplinary communication and a convenient source of reference.
期刊最新文献
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