基于离散型二项式系数组合模型的黄土湿陷性评估
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TU444

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河南省自然科学基金 (212300410280)


Evaluation of loess collapsibility based on discrete binomial coefficient combination model
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the National Science Foundation of Henan Province

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    摘要:

    为了快速精确地利用基本物性指标预测湿陷性黄土的湿陷性系数,基于多种数据挖掘方法提出了离散型二项式系数组合预测模型。首先,采用相关系数法和随机森林重要性指数法综合选取模型基本物性指标为饱和度、干密度、液性指数和天然含水量;然后,分别利用多元线性回归、BP神经网络、支持向量机回归(SVR)和随机森林(RF)回归对黄土湿陷性系数进行预测,并将预测结果进行组合,得到4种单一模型、2种传统组合模型和离散型二项式系数组合模型预测结果;最后,利用6种不同精度指标对上述7种预测模型展开精度分析。结果表明:组合预测模型精度整体高于单一预测模型,且提出的离散型二项式系数组合模型各精度指标均为最优,平均相对误差为3.43%。可见提出的离散型二项式系数组合模型可为湿陷性黄土地区的工程设计提供参考。

    Abstract:

    In order to quickly and accurately predict the collapsibility coefficient of collapsible loess from soil basic physical properties, a discrete binomial coefficient combined prediction model is proposed based on a variety of data mining methods. More specifically, initially, soil basic physical properties including the degree of saturation, dry density, liquidity index and natural water content were selected as input parameters of various prediction models according to correlation coefficient method and the random forest importance index method. Secondly, after predicting the collapsibility coefficients respectively through multiple linear regression model, BP neural network model, support vector machine regression (SVR) model and random forest (RF) regression model, the prediction results of four single models, two traditional combination models and the discrete binomial coefficient combination models could be obtained by combining the original predicted results. Finally, the accuracy of all seven prediction models discussed above were evaluated through six different accuracy indicators. The results show that the overall accuracy of the combined prediction models is higher than that of the single prediction models. All six accuracy indicators indicate the proposed discrete binomial coefficient combined model are the optimum, with an average relative error of 3.43%. In conclusion, the proposed discrete binomial coefficient combined model can provide a reference for the engineering designs in collapsible loess areas.

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任文博,刘云龙,李佳佳,等. 基于离散型二项式系数组合模型的黄土湿陷性评估[J]. 科学技术与工程, 2022, 22(12): 4945-4953.
Ren Wenbo, Liu Yunlong, Li Jiajia, et al. Evaluation of loess collapsibility based on discrete binomial coefficient combination model[J]. Science Technology and Engineering,2022,22(12):4945-4953.

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  • 收稿日期:2021-08-06
  • 最后修改日期:2022-01-20
  • 录用日期:2021-12-03
  • 在线发布日期: 2022-05-07
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