Grants and Contributions:

Title:

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

Agreement Number:

EGP

Agreement Value:

$25,000.00

Agreement Date:

Aug 23, 2017 -

Organization:

Natural Sciences and Engineering Research Council of Canada

Location:

Ontario, CA

Reference Number:

GC-2017-Q2-00545

Agreement Type:

Grant

Report Type:

Grants and Contributions

Additional Information:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Recipient's Legal Name:

Joksimovic, Darko (Ryerson University)

Program:

Engage Grants for Universities

Program Purpose:

Watershed flow monitoring data are widely used by municipalities and engineering firms as the bases forx000D
designing, sizing, monitoring, controlling, and optimizing drainage and sewer systems. Hence, the correctnessx000D
and completeness of such time-series plays a significant role in water resources management and engineeringx000D
and as such it is crucial to ensure data generated from flow sensors and rain gauges are accurate and reliable.x000D
Hydrological events and associated datasets are of a complex nature and various sources of error along withx000D
sensors malfunctioning or technical issues may result in gaps and irregularities within logged data which willx000D
then limit the use of generated datasets. Hence, it is necessity to adapt a reliable and efficient algorithm that canx000D
identify data abnormalities and correct faulty datasets. The proposed methodology aims to generate anx000D
automated algorithm that by employing statistical and mathematical analysis can identify and flag errors withinx000D
logged data series. Following that, faulty data will be reconstructed and replaced by means of applicablex000D
hydraulic and hydrological equations and or statistical estimation. The partner company, Civica Infrastructurex000D
Inc., has developed a powerful web-based, purpose-built data-storage platform named DataCurrent thatx000D
provides efficient and reliable ways to collect, process and analyze time-series watershed data. Despitex000D
DataCurrent's robustness, data is often manually inspected to identify and correct or remove incorrect data.x000D
This is a time consuming and costly process.The outcome of this project aside from its social andx000D
environmental gains will bring about significant benefits through automation of flow monitoring data qualityx000D
assurance and control (QA/QC) processes within the DataCurrent software, reduction of the time and costx000D
associated with manual data correction procedures and enhancement of the quality and reliability of thex000D
collected data.