An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation

文献类型： 外文期刊

第一作者： Li, Xiangyu

作者： Li, Xiangyu;Qin, Jing;Lu, Jingxuan;Lei, Tianjie;Qian, Guansheng;Wang, Jiabao;Wang, Weiwei;Chen, Dongpan

作者机构：

关键词： prediction; combination model; fisher optimal segmentation; goodness-of-fit weight; multi-scale; high slope

期刊名称：WATER （ 影响因子：3.53； 五年影响因子：3.628 ）

ISSN：

年卷期： 2022 年 14 卷 22 期

页码：

收录情况： SCI

摘要： Slope collapse is one of the most severe natural disaster threats, and accurately predicting slope deformation is important to avoid the occurrence of disaster. However, the single prediction model has some problems, such as poor stability, lower accuracy and data fluctuation. Obviously, it is necessary to establish a combination model to accurately predict slope deformation. Here, we used the GFW-Fisher optimal segmentation method to establish a multi-scale prediction combination model. Our results indicated that the determination coefficient of linear combination model, weighted geometric average model, and weighted harmonic average model was the highest at the surface spatial scale with a large scale, and their determination coefficients were 0.95, 0.95, and 0.96, respectively. Meanwhile, RMSE, MAE and Relative error were used as indicators to evaluate accuracy and the evaluation accuracy of the weighted harmonic average model was the most obvious, with an accuracy of 5.57%, 3.11% and 3.98%, respectively. Therefore, it is necessary to choose the weighted harmonic average model at the surface scale with a large scale as the slope deformation prediction combination model. Meanwhile, our results effectively solve the problems of the prediction results caused by the single model and data fluctuation and provide a reference for the prediction of slope deformation.

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