摘要
传统大坝预测方法难以适应坝体变形序列的高维非线性特征,且仅能以点值的形式预测大坝变形,未能有效量化由数据随机噪声、输入样本的主观确定、参数的随机选择等引起的结果不确定性。针对上述问题,提出了基于Bootstrap和改进布谷鸟优化多核极限学习机(ICS-MKELM)算法的大坝变形预测模型,实现在精确预测大坝变形点值的同时,通过区间形式量化预测值的不确定性。首先,建立基于高精度多核极限学习机(MKELM)的大坝变形预测模型,该模型集成了核极限学习机(KELM)高效处理强非线性回归问题的优势和混合核泛化、学习能力强的特点,同时采用基于惯性权重和混沌理论改进的布谷鸟搜索(ICS)算法对多核极限学习机中核参数及正则系数进行优化,弥补模型易陷入局部最优的不足;其次,引入Bootstrap区间预测方法对模型和数据造成的不确定影响进行量化;最后,将所提模型应用于某实际大坝工程的变形预测,分析了不同训练样本数对模型预测精度的影响,同时通过与五种常用的预测算法进行对比,验证了本文模型具有一致性和优越性。
Traditional prediction methods are hardly applicable to the dam deformation featured with high dimensions and nonlinearity;they can predict the deformation at location points of a dam body,but fail to effectively quantify the uncertainties from data with random noise,subjectivity in input samples,and randomness in parameter selection.To solve this problem,we develop a new dam deformation prediction model based on the Bootstrap algorithm and an improved cuckoo search–multiple kernel extreme learning machine(ICS-MKELM)algorithm.The model quantifies the uncertainty through interval prediction and can realize accurate point prediction of dam deformation.First,based on high-precision MKELM,we construct a dam deformation prediction model that integrates the advantage of KELM efficiently handling strong nonlinear regression problems with the superiority of a hybrid kernel of high generalization and strong learning capability.And an ICS algorithm,based on the inertia weight and chaos theory,is adopted to optimize MKELM’s kernel parameters and regular coefficients,offsetting its disadvantage of easy falling into local optimization.Then,a Bootstrap interval prediction method is used to quantify the uncertainty from the model and data.Our model is applied to the deformation prediction of a real dam,and its consistency and superiority are demonstrated through an analysis on the influence of different sizes of the training sets on prediction accuracy and a comparison with other five commonly-used prediction algorithms.
作者
王晓玲
谢怀宇
王佳俊
陈文龙
蔡志坚
刘宗显
WANG Xiaoling;XIE Huaiyu;WANG Jiajun;CHEN Wenlong;CAI Zhijian;LIU Zongxian(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300072)
出处
《水力发电学报》
EI
CSCD
北大核心
2020年第3期106-120,共15页
Journal of Hydroelectric Engineering
基金
国家重点研发计划(2018YFC0407101)
国家自然科学基金创新研究群体项目(51621092)
国家自然科学基金(51839007).
关键词
大坝变形
区间预测
多核极限学习机
改进布谷鸟搜索算法
不确定性
dam deformation
interval prediction
multiple-kernel extreme learning machine
improved cuckoo search algorithm
uncertainty
作者简介
王晓玲(1968—),女,教授.E-mail:wangxl@tju.edu.cn;通信作者:王佳俊(1991—),男,助理研究员.E-mail:jiajun_2014_bs@tju.edu.cn
