基于机器学习的跨海管道泄漏位置预测模型
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TU991;TV698. 1+2

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Leakage location prediction model of trans-oceanic pipelines based on machine learning
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    摘要:

    为解决跨海管道泄漏位置定位问题,利用EPANET 软件对海底管道泄漏探测进行建模,采用BP 神经网络模型和经K-CV 改进的SVR 模型进行泄漏位置预测。对BP 神经网络的隐含层数和学习函数进行优化和选择,使用K-CV 方法对SVR 算法的惩罚系数c 和核函数参数g 进行最优组合探寻。利用EPANET 软件建模数据形成训练集,随机选取测试集进行预测,同时使用均方根误差和相关系数对预测结果进行评价。实例验证结果表明:K-CV 方法能够有效提高SVR 模型预测精度;与水力学稳态方程相比,BP 神经网络模型在泄露位置预测问题中应用范围更广、预测精度更高。

    Abstract:

    To solve the problem of pipeline leakage location determination, the leak situation of a trans-oceanic pipeline was modeled by EPANET software, and the BP neural network model and K-CV improved SVR model were used to predict the leak location. Firstly, the number of hidden layers and learning function of BP neural network were optimized and selected, and K-CV method was used to search for the optimal combination of the penalty coefficient c and the kernel function parameter g of SVR. The EPANET software modeling data was used to form a training set, and the test set was randomly selected from original data for prediction. Meanwhile, the relative mean square error and correlation coefficient were used to evaluate the prediction results. The experimental results show that K-CV method can effectively improve the accuracy of SVR model. Compared with the traditional steady-state hydraulic equation, the neural network model has a wider range of applications and higher accuracy in the prediction of leak location of trans-oceanic pipelines.

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卓美燕,林文介.基于机器学习的跨海管道泄漏位置预测模型[J].水利水电科技进展,2022,42(3):45-50.(ZHUO Meiyan, et al. Leakage location prediction model of trans-oceanic pipelines based on machine learning[J]. Advances in Science and Technology of Water Resources,2022,42(3):45-50.(in Chinese))

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  • 在线发布日期: 2022-05-16
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