Developing an Automated Reliable Methodology for the Detection and Correction of Error within a Sanitary and Storm Water Data Collection and Processing System

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Watershed flow monitoring data are widely used by municipalities and engineering firms as the bases fordesigning, sizing, monitoring, controlling, and optimizing drainage and sewer systems. Hence, the correctnessand completeness of such time-series plays a significant role in water resources management and engineeringand as such it is crucial to ensure data generated from flow sensors and rain gauges are accurate and reliable.Hydrological events and associated datasets are of a complex nature and various sources of error along withsensors malfunctioning or technical issues may result in gaps and irregularities within logged data which willthen limit the use of generated datasets. Hence, it is necessity to adapt a reliable and efficient algorithm that canidentify data abnormalities and correct faulty datasets. The proposed methodology aims to generate anautomated algorithm that by employing statistical and mathematical analysis can identify and flag errors withinlogged data series. Following that, faulty data will be reconstructed and replaced by means of applicablehydraulic and hydrological equations and or statistical estimation. The partner company, Civica InfrastructureInc., has developed a powerful web-based, purpose-built data-storage platform named DataCurrent thatprovides efficient and reliable ways to collect, process and analyze time-series watershed data. DespiteDataCurrent's robustness, data is often manually inspected to identify and correct or remove incorrect data.This is a time consuming and costly process.The outcome of this project aside from its social andenvironmental gains will bring about significant benefits through automation of flow monitoring data qualityassurance and control (QA/QC) processes within the DataCurrent software, reduction of the time and costassociated with manual data correction procedures and enhancement of the quality and reliability of thecollected data.