基于GA-PSO-XGBoost模型的城市燃气管道风险评价方法

彭善碧; 王鸿扬; 郑鹤

doi:10.3969/j.issn.1007-3426.2026.02.019

基于GA-PSO-XGBoost模型的城市燃气管道风险评价方法

Risk assessment method of urban gas pipeline based on GA-PSO-XGBoost model

摘要

摘要:
目的针对传统风险评价方法在城市燃气管道风险评价中存在的定量分析困难、主观性强等局限，提出一种基于机器学习的燃气管道风险评价模型。
方法融合遗传算法（genetic algorithm，GA）、粒子群优化（particle swarm optimization，PSO）算法和极限梯度提升（eXtreme gradient boosting，XGBoost）算法，构建了GA-PSO-XGBoost城市燃气管道风险评价模型。该模型利用GA-PSO混合算法优化XGBoost的超参数，以提升风险评价的精度。
结果模型测试结果表明，GA-PSO-XGBoost模型的均方误差（mean square error，MSE）、平均绝对误差（mean absolute error，MAE）和决定系数（R²）分别为4.1264、1.4939和99.1146%，验证了模型具有高精度与可靠性。
结论 GA-PSO-XGBoost模型为城市燃气管道的风险评价提供了一种新方法，对提升燃气管道安全管理水平具有重要应用价值。

Abstract:
Objective Aiming at the limitations of the traditional risk assessment methods in the risk assessment of urban gas pipelines, such as the difficulty of quantitative analysis and strong subjectivity, a gas pipeline risk assessment model based on machine learning is proposed.
Method Based on the genetic algorithm (GA), particle swarm optimization (PSO), and eXtreme gradient boosting (XGBoost) algorithm, the GA-PSO-XGBoost urban gas pipeline risk assessment model is constructed. The model uses the GA-PSO hybrid algorithm to optimize the hyperparameters of XGBoost, thereby improving the accuracy of urban gas pipeline risk assessment.
Result The model test results show that the mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²) of the GA-PSO-XGBoost model are 4.1264, 1.4939 and 99.1146%, respectively, which verifies the high accuracy and reliability of the model.
Conclusion The GA-PSO-XGBoost model provides a new method for the risk assessment of urban gas pipeline, which has an important application value for improving the safety management of gas pipeline.

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