结构工程师2024，Vol.40Issue(4)：39-47,9.
供水管网故障预测的机器学习方法
Machine Learning Methods to Predict Failures in Water Distribution Network
摘要
Abstract
In recent years,failures have occurred frequently to water distribution networks throughout China,resulting in huge economic losses and serious social impacts.However,the application of state-of-art algorithms to predict pipe failures is still to be explored and there is little research on systematic comparison of machine learning algorithms.Therefore,it is of great importance to accurately predict pipeline failures and ensure the operational safety of water distribution networks economically and efficiently.To this end,the failure prediction problem is described.The basic theories of four machine learning algorithms,i.e.,logistic regression,random forest,artificial neural network and 1-D convolution neural network,are introduced.The failure prediction performance of the four models is verified and compared with a case network in an industrial park of a city in the south of China.The results show that the 1-D convolutional neural network has the highest accuracy but random forest is the most cost-effective algorithm.The impact of each pipe feature on the failure probability is analyzed as well.It is found that pipe diameter,pipe length,road class and qualification of construction enterprise are the four most important features.The failure probability is negatively correlated with pipe diameter and road class while positively correlated with pipe length and qualification of construction enterprise.关键词
供水管网/故障预测/机器学习/管道特征影响Key words
water pipes/failure prediction/machine learning/impact of pipe feature引用本文复制引用
刘威,谢志印..供水管网故障预测的机器学习方法[J].结构工程师,2024,40(4):39-47,9.基金项目
"十四五"国家重点研发计划(2022YFC3801000) （2022YFC3801000）
上海市2022年度"科技创新行动计划"社会发展科技攻关项目(22dz1201201) （22dz1201201）
