基于 APSO 和 TWSVM 的露天矿高陡边坡地表变形预测模型

Sunwen Du, Ruiting Song, Qing Qu, Zhiying Zhao, Hailing Sun, Yanwei Chen
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引用次数: 0

摘要

目前,由于地质条件复杂多变,高陡边坡变形精确预报技术无法实现精确预报。尤其是单一的预测模型存在稳定性差、精度低、数据波动大等问题。在实践中,挖掘露天矿坡面变形监测数据与各种影响因素之间复杂的非线性关系,提高高陡边坡变形预测精度是露天矿安全生产的关键。该研究提出在孪生支持向量机(TWSVM)中引入位置因子和速度因子。选择自适应子群优化(APSO)算法进行参数优化。通过对 TWSVM、遗传算法-TWSVM(GA-TWSVM)和所提出的 APSO⁃TWSVM 的对比分析,实验数据表明三种模型的平均绝对误差(MAE)值分别为 13.29 %、8.17 %和1.27 %,均方根误差(RMSE)分别为47.83 %、6.52 %和3.02 %;APSO⁃TWSVM的预测时间比GA-TWSVM提高了62.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM
At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %,8.17 %, and 1.27 %, the RMSE - 47.83 %,6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.
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