Development of a Sustainable National Sewer Inventory

Lewis, Phil; Khaleghian, Hossein; Shan, Yongwei

doi:10.1016/j.proeng.2016.04.177

Cited by 4 publications

(1 citation statement)

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“…The objective of data quality evaluation is to ensure that the inspection data are accurate and consistent with other datasets. This process is a significant step in developing sewer systems data inventory by integrating existing datasets [22,23].…”

Section: Introductionmentioning

confidence: 99%

Developing a Data Quality Evaluation Framework for Sewer Inspection Data

Khaleghian¹,

Shan²

2023

Water

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The increasing amount of data and the growing use of them in the information era have raised questions about the quality of data and its impact on the decision-making process. Currently, the importance of high-quality data is widely recognized by researchers and decision-makers. Sewer inspection data have been collected for over three decades, but the reliability of the data was questionable. It was estimated that between 25% and 50% of sewer inspection data is not usable due to data quality problems. In order to address reliability problems, a data quality evaluation framework is developed. Data quality evaluation is a multi-dimensional concept that includes both subjective perceptions and objective measurements. Five data quality metrics were defined to assess different quality dimensions of the sewer inspection data, including Accuracy, Consistency, Completeness, Uniqueness, and Validity. These data quality metrics were calculated for the collected sewer inspection data, and it was found that consistency and uniqueness are the major problems based on the current practices with sewer pipeline inspection. This paper contributes to the overall body of knowledge by providing a robust data quality evaluation framework for sewer system data for the first time, which will result in quality data for sewer asset management.

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Section: Introductionmentioning

confidence: 99%