基于改进PSO-RF算法的大坝变形预测模型  

Dam deformation prediction model based on improved PSO-RF algorithm

作　　者：张石[1] 郑东健[1] 陈卓研 ZHANG Shi;ZHENG Dongjian;CHEN Zhuoyan(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China)

机构地区：[1]河海大学水利水电学院,江苏南京210098

出　　处：《水利水电科技进展》2022年第6期39-44,共6页Advances in Science and Technology of Water Resources

基　　金：国家自然科学基金(52179128)。

摘　　要：针对传统随机森林参数寻优方法的不足,引入均衡惯性权重和自适应变异对粒子群优化算法进行改进,提出了一种基于改进粒子群优化算法和随机森林算法(改进PSO-RF算法)的大坝变形预测模型。实例验证结果表明,在计算效率方面,与传统网格搜索法相比,改进PSO-RF算法显著提升了模型的寻优速度;在预测精度和稳定性方面,基于改进PSO-RF算法的大坝变形预测模型明显优于长短期记忆网络、支持向量机和BP神经网络模型。In view of the shortcomings of traditional random forest parameter optimization methods,the particle swarm optimization algorithm was improved by introducing equalizing inertia weight and adaptive mutation,and a dam deformation prediction model based on improved particle swarm optimization algorithm and random forest algorithm(improved PSO-RF algorithm)was proposed.The example analysis shows that in terms of computational efficiency,compared with traditional grid search method,the improved PSO-RF algorithm significantly improves the optimization speed of the model.In the aspects of prediction accuracy and stability,the dam deformation prediction model based on the improved PSO-RF algorithm is obviously better than long short-term memory,support vector machine and BP neural network.

关 键 词：随机森林 变形预测 粒子群优化 惯性权重 自适应变异 

分 类 号：TV698.1[水利工程—水利水电工程]