基于PDO-SVR的施工项目风险预测模型  

Engineering Project Risk Prediction Model Based on Prairie Dog Optimizes Support Vector Regression Machine

作　　者：周来俭 陈小卫 ZHOU Laijian;CHEN Xiaowei(Department of Space Support,Space Engineering University,Beijing 101416,China;China Xi’an Satellite Control Center,Xi’an 710043,China)

机构地区：[1]航天工程大学航天保障系,北京101416 [2]中国西安卫星测控中心,陕西西安710043

出　　处：《计算机与网络》2024年第5期466-470,共5页Computer & Network

摘　　要：在施工项目领域,有效风险预测对于施工项目的顺利完成至关重要。针对传统风险预测模型难以实现非线性条件下的风险预测问题,提出了一种基于土拨鼠优化算法支持向量回归机(Prairie Dog Optimization Algorithm Optimizes Support Vector Regression Machine,PDO-SVR)的施工项目风险预测模型。该模型利用SVR强大的非线性预测能力,对施工项目的风险进行预测,针对人工选择SVR参数存在不合理的问题,利用PDO对SVR参数进行优化。实验结果表明,PDO-SVR模型具有更低的预测误差和良好的预测效果。In the field of engineering projects,effective risk prediction is crucial for the smooth completion of engineering projects.To address the challenge of traditional risk prediction models not being able to achieve risk prediction under non-linear conditions,a new engineering project risk prediction model based on Prairie Dog Optimization Algorithm Optimizes Support Vector Regression Machine(PDO-SVR)is proposed.This model uses the powerful non-linear prediction ability of SVR to predict the engineering project risk.Aiming at the unreasonable problem of manual selection of SVR parameters,PDO is used to optimize SVR parameters.Experimental results demonstrate that the PDO-SVR model has lower prediction errors and good prediction performance.

关 键 词：施工项目 风险预测 土拨鼠优化算法 支持向量回归机 小型数据集 

分 类 号：F283.7[经济管理—国民经济]