丁东水库边坡变形预测研究
Research on Slope Deformation Prediction of the Dingdong Reservoir
付春华 1张璐1
作者信息
- 1. 山东省德州市水利局,山东 德州 253000
- 折叠
摘要
对水库边坡变形进行预测对于确保水库大坝的安全运行具有重要意义,本研究旨在提高水库边坡变形的预测精度,为确保水库大坝的安全运行提供技术支持.文章采用 PSO-GM(1,1)方法,将粒子群优化(PSO)算法引入灰色预测模型,借助PSO算法的全局优化能力和快速收敛性优化背景值和参数,提高了预测精度;以丁东水库边坡监测数据为样本,构建PSO-GM(1,1)模型进行实例分析.结果表明:该模型的预测值与实测值吻合,在短期内具有较高的预测精度,可为水库边坡防护决策提供准确参考依据.研究结果不仅为丁东水库的安全运行提供了技术支持,也为类似工程提供经验和借鉴.
Abstract
Predicting reservoir slope deformation is of great significance for ensuring the safe operation of reservoir dams.This paper aims to improve the accuracy of reservoir slope deformation predictions and provide technical support for the safe operation of reservoir dams.The PSO-GM(1,1)method is employed,where the Particle Swarm Optimization(PSO)algorithm is introduced into the grey prediction model.By utilizing the global optimization capability and fast convergence of the PSO algorithm to optimize background values and parameters,the prediction accuracy is enhanced.Using the monitoring data of the Dingdong Reservoir slope as a sample,the PSO-GM(1,1)model is constructed for case analysis.The results show that the model's predicted values match well with the actual measurements,demonstrating high prediction accuracy in the short term.This can provide accurate reference data for decision-making in reservoir slope protection.The research results not only provide technical support for the safe operation of the Dingdong Reservoir but also lay a theoretical foundation for slope deformation prediction in similar projects.
关键词
水库边坡/预测/PSO-GM(1,1)/灰色预测/粒子群优化Key words
reservoir slope/prediction/PSO-GM(1,1)/grey prediction/Particle Swarm Optimization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据