Handling incomplete and missing data in water network database using imputation methods
Handling incomplete and missing data in water network database using imputation methods
复制标题
使用插补方法处理水网络数据库中的不完整和缺失数据
DOI：
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发表时间：
2020
影响因子：
5.9
通讯作者：
R. Sadiq
中科院分区：
文献类型：
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作者：
G. Kabir;S. Tesfamariam;Jordi Hemsing;R. Sadiq
摘要：如果供水管网数据不完整、不完整或缺失，制定广泛的供水管网更新计划或风险管理行动计划是一项挑战。对于中小型水务公司来说，投资于现有水管的广泛检查和数据收集计划以填补数据空白可能不符合成本效益。在这项研究中，三种单一插补方法的性能（即，平均值插补、中值插补和基于线性回归）和三种多重插补方法（即，比较了基于迭代稳健模型的插补（IRMI）、不完全多变量数据的多重插补（AMELIA）和缺失值的序贯插补（IMPSEQ）。分析了加拿大阿尔伯塔省卡尔加里市供水管网铸铁管数据。结果表明，IMPSEQ方法执行最好的插补缺失值在水网络数据库相比，其他单一和多重插补方法。
ABSTRACT It is challenging to develop an extensive water mains renewal program or risk management action plan if there is incomplete, partial or missing water network data. For small and medium-sized water utilities, it may not be cost effective to invest in extensive inspection and data collection programs on existing water mains to fill data gaps. In this study, the performance of three single imputation methods (i.e., mean imputation, median imputation, and linear regression-based) and three multiple imputation methods (i.e., iterative robust model-based imputation (IRMI), multiple imputations of incomplete multivariate data (AMELIA), and sequential imputation for missing values (IMPSEQ)) are compared. The cast iron (CI) water mains data of the water distribution network (WDN) of the City of Calgary, Alberta, Canada is analyzed. Results indicate that the IMPSEQ method performed best with respect to imputing missing values in water network databases compared to the other single and multiple imputations methods used.