Research On Corrosion Rate Prediction Of Marine Pipeline Based On Ensemble Learning

With the expansion of offshore oil and gas pipeline laying scope and the growth of service time,the problem of pipeline corrosion failure has become increasingly prominent,posing a huge threat to the safety of the marine ecological environment and people's properties.This study combines engineering safety with information technology,and introduces Ensemble Learning algorithm to reasonably and accurately predict the corrosion rate of marine oil and gas pipeline,so as to grasp the overall situation of offshore oil and gas pipeline corrosion and provide reference for corrosion protection of offshore oil and gas pipeline.The paper is divided into three parts: Firstly,due to the difficulty of data acquisition of offshore oil and gas pipeline and the small amount of data,the data of corrosion rate of offshore pipeline is expanded,and the correlation between corrosion variables of offshore pipeline is distinguished.Because there is a certain correlation between individual factors,the dimension of corrosion factors of offshore pipeline is reduced by principal component analysis,and GBDT is used to output characteristics Then,three Ensemble Learning algorithms are introduced into the corrosion prediction of offshore pipeline,and the prediction model of corrosion rate of offshore pipeline is built based on RF,GBDT and XGBoost,in which the XGBoost algorithm with complex parameters is adopted The Stacking model is built on the basis of the three models.The K-fold cross-validation is used to analyze the model of the marine pipeline corrosion.The model is compared with the fusion model,and the accuracy of the Stacking fusion model is shown.Finally,suggestions on corrosion protection of offshore oil and gas pipelines are put forward from the perspective of management.The research results show that the three corrosion rate prediction models based on Ensemble Learning all show good prediction effects and model characteristics.RF has high calculation efficiency and the XGBoost model has the best prediction accuracy among the three models.The prediction effect of GBDT in the default parameters is between the two;the average results of the stacking fusion model in single prediction and K-fold cross-validation are better than the three models,showing better prediction effect and stability,And enrich the prediction methods of marine pipeline corrosion rate,and provide reference for the corrosion maintenance and protection of marine oil and gas pipelines.