基于改进随机森林模型的海底管道腐蚀预测

Corrosion prediction of submarine pipelines based on improved Random Forest model

为提高海底管道腐蚀速率预测精度,建立一种基于改进随机森林的海底管道腐蚀预测模型。首先,采用斯皮尔曼相关系数,分析实海挂片腐蚀数据的相关性,并采用因子分析降维;然后,设定K值为5的K折交叉验证,建立随机森林回归(RFR)模型,并输入降维后的数据;最后,输出模型的交叉验证得分,并对比评价该模型与其他模型的最高得分预测结果。研究结果表明:改进RFR的五折交叉验证输出平均得分值为0.912,高于BP神经网络(BPNN)模型、支持向量回归(SVR)模型2种对比模型;五折交叉验证最高得分预测结果均方根误差(RMSE)、平均绝对误差(MAE)分别为1.441和1.3,均优于对比模型相应值。

In order to improve prediction accuracy of corrosion rate of submarine pipelines,a submarine pipeline corrosion prediction model based on improved Random Forest was established. First,Spearman correlation coefficient was used to analyze correlation of corrosion data of real Marine hanging plates and factor analysis was performed to reduce dimensionality. Then,the K K-fold cross-Nalidation was set to five,K-fold cross-validation was set up,and RFR model was established to train dimensionality reduction data. Finally,output model ’ s cross-validation score,and compare and evaluate model ’ s highest score prediction result with other models. The research results show that average score of improved RFR model is0. 912,which is higher than BP neural network( BPNN) model and support vector regression( SVR)model. Meanwhile,root mean square error( RMSE) and mean absolute error( MAE) of the highest score prediction results of five-fold cross-validation are 1. 441 and 1. 3 respectively,which are better than corresponding values of contrast model.