A Pipeline Blockage Identification Model Learning from Unbalanced Datasets Based on Random Forest

被引：3

作者：

Fang Mingyue ^{[1
,2
]}

Feng Zao ^{[1
,2
]}

Wang Xiaodong ^{[1
,2
]}

Ma Jun ^{[1
,2
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650500, Yunnan, Peoples R China

[2] Kunming Univ Sci & Technol, Yunnan Prov Key Lab Artificial Intelligence, Kunming 650500, Yunnan, Peoples R China

来源：

PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年

关键词：

Unbalanced data; Drainage pipes; Blockage status identification; Random forest;

D O I：

10.1109/CCDC52312.2021.9602663

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at the problem of decreased accuracy of operating state recognition caused by the unbalanced data acquisition between the normal and blocked failure states of urban drainage pipelines, a new method of pipeline blockage state recognition based on unbalanced data is proposed. The experimental results show that the random forest algorithm, which uses bootstrap sampling and simple voting methods to integrate decision trees, has a good effect on the pipeline blocking state recognition of unbalanced data.

引用

页码：696 / 701

页数：6

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