{"title":"Predicting the Deformation of a Slope Using a Random Coefficient Panel Data Model","authors":"Zhenxia Yuan, Yadong Bian, Weijian Liu, Fuzhou Qi, Haohao Ma, Sen Zheng, Zhenzhu Meng","doi":"10.3390/fractalfract8070429","DOIUrl":null,"url":null,"abstract":"Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.","PeriodicalId":510138,"journal":{"name":"Fractal and Fractional","volume":"11 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fractal and Fractional","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/fractalfract8070429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.
沿海地区的工程建设不仅会影响现有的滑坡,还会诱发新的滑坡。水位的变化使沿海地区成为地质灾害的易发区。基于监测数据的斜坡位移预测在潜在滑坡和斜坡崩塌的早期预警中发挥着重要作用,并为灾害风险管理提供支持。鉴于边坡变形的复杂特性,我们在面板数据分析的框架下,提出了一种采用随机系数模型的预测模型,以考虑监测点之间的相关性。此外,我们还利用高斯混合模型对监测数据进行了分类,以考虑时空特征。我们利用郭布坡的监测数据对模型进行了验证。结果表明,所提出的模型在预测精度方面有更好的表现。我们还将提出的模型与 BP 神经网络模型和时间-温度模型进行了比较,发现提出的模型的预测精度优于两个对照模型。