Zhang Junrong, Tang Huiming, Tannant Dwayne D, Lin Chengyuan, Xia Ding, Wang Yankun, Wang Qianyun
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China.
Three Gorges Research Center for Geohazards of Ministry of Education, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2021 Dec 14;21(24):8352. doi: 10.3390/s21248352.
With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and -test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.
随着机器学习方法的广泛应用，不断提高预测精度已成为一项重要任务，这对于滑坡位移预测尤为关键。本研究旨在基于多种新算法的组合，提出一种新颖的预测模型，以提高滑坡预测的准确性。所提出的新方法包括三个部分：数据准备、多群体智能（MSI）优化和位移预测。在数据准备中，采用完全集合经验模态分解（CEEMD）从观测到的累积滑坡位移中分离出趋势位移和周期性位移。与滑坡运动相关的重构诱发因素的频率分量和残差分量也通过CEEMD和检验提取出来，然后用实序列编辑距离（EDR）挑选出来作为支持向量回归（SVR）模型的输入变量。在MSI优化中使用MSI优化算法对SVR模型进行优化；这样，可以得到六个可用于位移预测部分的预测模型。最后，分别用六个优化后的SVR模型预测趋势位移和周期性位移。将预测精度最高的趋势位移和周期性位移相加，作为最终预测结果。十里树堡滑坡的案例研究表明，预测结果与观测数据吻合良好，平均相对误差有所改善，这表明所提出的模型能够高精度地预测滑坡位移，即使位移呈现受多个时变因素影响的阶梯状曲线特征。
