A survey on data imputation techniques: Water distribution system as a use case

Abstract

The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a "top-down bottom-up'' two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.