摘要
滑坡位移高精度预测对于滑坡预测预警具有重要的参考价值。顾及智能优化与机器学习组合模型在滑坡时序位移预测中的优势,构建了一种融合多层感知机和优化支持向量回归的滑坡位移组合预测模型。首先采用多层感知机(multilayer perceptron,MLP)对滑坡位移进行初步预测,然后构建基于差分进化(differential evolution,DE)算法改进的人工鱼群算法(artificial fish swarm algorithm,AFSA)与支持向量回归(support vector regression,SVR)组合预测模型(optimal combination SVR,OPSVR)以修正MLP预测结果。通过两起典型滑坡体北斗实测算例发现,由于DE有效克服了AFSA运行后期人工鱼个体大多处于随机运动状态而无法搜索到全局最优解的问题,提高了其寻优性能,进一步与SVR结合可更合理确定出SVR的超参数,从而提高了其预测精度;相较于单一MLP和SVR预测模型,以及常规智能优化算法(遗传算法、粒子群算法)、改进人工鱼群算法与SVR的组合预测模型,MLP-OPSVR组合预测模型具有更高精度的预测结果,且在滑坡预警研究中具有较好的推广应用价值。
Objectives:Landslide disaster has become the first geological disaster in China.In the process of landslide movement instability,its surface will show certain deformation information,and this deformation information is an important external manifestation of landslide internal change instability,which can be used as an important reference information for landslide prediction and early warning.Therefore,the high-preci⁃sion prediction of landslide displacement has important reference value for landslide disaster prediction and early warning.Methods:Considering the advantages of the intelligent optimization and machine learning al⁃gorithms,a landslide displacement combined prediction model is established.First,the multilayer percep⁃tron(MLP)method is used to preliminary prediction of landslide displacement.Then,the artificial fish swarm algorithm(AFSA)improved based on differential evolution(DE)algorithm is further constructed,and combined with the support vector regression(SVR)to established an optimal combination SVR(OPS⁃VR)to modify the prediction results calculated by MLP.Results:The two typical landslides monitored by BeiDou show that:(1)For HF08,the single SVR prediction model had a root mean square error(RMSE)of 23.8 mm between the prediction and the true values on the test set,and RMSE for MLP,genetic algo⁃rithm-SVR(GA-SVR),particle swarm optimization-SVR(PSO-SVR)and OPSVR prediction models are 19.9 mm,10.9 mm,8.3 mm and 8.3 mm,respectively.Compared with the SVR,MLP,GA-SVR,PSO-SVR and OPSVR prediction models,the prediction accuracy of MLP-OPSVR prediction model is improved by 6.2 times,5.0 times,2.3 times,1.5 times and 1.5 times,respectively.The RMSE of MLPOPSVR prediction model is only 3.3 mm.(2)For HF05,the single SVR prediction model had a RMSE of 26.5 mm between the prediction and the true values on the test set,and RMSE for MLP,GA-SVR,PSO-SVR and OPSVR prediction models are 32.8 mm,15.4 mm,15.6 mm and 15.5 mm,respectively.However,the RMSE of MLP-OPSVR prediction model is only 7.0 mm.Compared wi
作者
李达
瞿伟
张勤
李久元
凌晴
LI Da;QU Wei;ZHANG Qin;LI Jiuyuan;LING Qing(College of Geology Engineering and Geomatics,Chang'an University,Xi'an 710054,China;State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China;College of Civil Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《武汉大学学报（信息科学版）》
EI
CAS
CSCD
北大核心
2023年第8期1380-1388,共9页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(42174006,42090055)
陕西省杰出青年科学基金(2022JC-18)
地质灾害防治与地质环境保护国家重点实验室开放基金(SKLGP2021K022)
长安大学中央高校基本科研业务费专项资金资助(300102263201,300102262902)。
关键词
滑坡位移预测
多层感知机
支持向量回归
差分进化算法
人工鱼群算法
landslide displacement prediction
multilayer perceptron(MLP)
support vector regression(SVR)
differential evolution(DE)algorithm
artificial fish swarm algorithm(AFSA)
