基于随机森林算法的大坝安全监测研究

Research on dam safety monitoring based on random forest algorithm

基于大坝的安全运行监测数据,运用智能算法构建大坝安全监测模型,力求精度高、稳定性好的大坝安全监测预测模型,以便能更及时准确地预测大坝可能存在的安全风险。针对大坝安全监测数据构建了基于随机森林算法的大坝变形监测模型,通过实际案例对某大坝进行建模,根据训练拟合数据与实测值比较,分析了其拟合度与误差,作出了未来变形预测分析。与BP神经网络算法的大坝变形监测模型进行对比,结果表明基于随机森林算法较BP神经网络算法预测精度较高,稳定性较好,综合预测能力更强,为大坝位移变形预测提供了一种新途径。

Based on the safety operation monitoring data of dams,an intelligent algorithm is used to construct a dam safety monitoring model,striving for a high accuracy and good stability dam safety monitoring and prediction model,in order to more timely and accurately predict the potential safety risks of the dam.A dam deformation monitoring model based on random forest algorithm was constructed for dam safety monitoring data.A certain dam was modeled through an actual case,and its fitting degree and error were analyzed by comparing the training fitting data with the measured values.Future deformation prediction analysis was made.Compared with the BP neural network algorithm,the dam deformation monitoring model based on the random forest algorithm has higher prediction accuracy,better stability,and stronger comprehensive prediction ability,providing a new approach for predicting dam displacement and deformation.