首页|Towards an improved prediction of soil-freezing characteristic curve based on extreme gradient boosting model
Towards an improved prediction of soil-freezing characteristic curve based on extreme gradient boosting model
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As an essential property of frozen soils,change of unfrozen water content(UWC)with temperature,namely soil-freezing characteristic curve(SFCC),plays significant roles in numerous physical,hydraulic and mechanical processes in cold regions,including the heat and water transfer within soils and at the land-atmosphere interface,frost heave and thaw settlement,as well as the simulation of coupled thermo-hydro-mechanical interactions.Although various models have been proposed to estimate SFCC,their applicability remains limited due to their derivation from specific soil types,soil treatments,and test devices.Accordingly,this study proposes a novel data-driven model to predict the SFCC using an extreme Gradient Boosting(XGBoost)model.A systematic database for SFCC of frozen soils compiled from extensive experimental investigations via various testing methods was utilized to train the XGBoost model.The predicted soil freezing characteristic curves(SFCC,UWC as a function of tempera-ture)from the well-trained XGBoost model were compared with original experimental data and three conventional models.The results demonstrate the superior performance of the proposed XGBoost model over the traditional models in predicting SFCC.This study provides valuable insights for future investiga-tions regarding the SFCC of frozen soils.
Soil freezing characteristic curve(SFCC)Soil temperatureUnfrozen water contentXGBoost modelMachine LearningFeature importance
Kai-Qi Li、Hai-Long He
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Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong,China
College of Natural Resources and Environment,Northwest A&F University,Yangling 712100,China
Department of Soil Science,University of Manitoba,Winnipeg,Manitoba,R3T 2N2 Canada
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