考虑天气因素的给水管道漏损预测模型

天气变化引起土壤含水率变化和变形等,导致气温变化与市政给水管道的破损事件存在相关性.为此,基于北方某城市给水管网破损事件数据和气温记录,分析不同的天气因素量化指标与管道破损事件的相关性.采用误差反向传播神经网络(BPNN)和基因表达式编程(GEP)方法,建立考虑天气因素的给水管道漏损预测模型.根据案例城市过去11年的市...

Failure prediction model of water distribution pipelines considering weather factors

Due to the changes in soil moisture content and deformation caused by weather changes, there is a correlation between weather temperature and failures of urban water distribution pipelines. Based on the failure records of pipelines and weather temperatures of a northern China city, the correlations between different weather temperature indicators and water mains failures were analyzed. The failure prediction models of water distribution pipelines considering weather temperature factors were established using error back propagation neural network (BPNN) and genetic expression programming (GEP) methods. According to the failure records of water distribution pipelines, pipeline geographic information, and weather temperature records of the case city in the past 11 years, the correlations among pipeline failures and six weather factors were analyzed, including average temperature, freezing indicator, maximum increase, maximum decrease, maximum increase rate, and maximum decrease rate. BPNN and GEP were used to establish the implicit and explicit relationships between the number of pipeline failures (i.e., the dependent variable) and four explanatory variables (the selected weather temperature indicator, the diameter, age, and length of pipelines). The explicit and implicit models were used to predict the number of pipeline failures in the case city in the next year. The determination coefficients of the prediction results of the model without considering weather factors were 0.65 and 0.60 respectively, while those considering weather factors were 0.78 and 0.88, where the prediction accuracy increased by 13% and 28%. Therefore, it is reasonable and effective to establish a failure prediction model of water distribution pipelines by considering weather factors.