Prediction of soil corrosion rate of buried pipeline based on KPCA-GWO-SVM

In order to improve the prediction accuracy of soil corrosion rate of buried pipelines, the influencing factors of soil corrosion were sorted out and analyzed. The data dimensionality of influencing factors was reduced by Kernel Principal Component Analysis (KPCA), the key parameters of the Support...

وصف كامل

التفاصيل البيبلوغرافية

المؤلفون الرئيسيون:	Changjing LIANG, Bo XIE, Yanqing LIU, Zhijuan LIU, Ziqiang GUO, Chunyan REN, Qiongtao CHEN, Yichi LIU
التنسيق:	Artigo
اللغة:	Chinês
منشور في:	Editorial Office of Oil & Gas Storage and Transportation 2021-08-01
سلاسل:	You-qi chuyun
الموضوعات:	buried pipeline soil corrosion kernel principal component analysis (kpca) grey wolf optimizer (gwo) support vector machine (svm) corrosion prediction
الوصول للمادة أونلاين:	http://yqcy.xml-journal.net/cn/article/doi/10.6047/j.issn.1000-8241.2021.08.016
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!