Reference number : GC-2017-Q2-00545, amendment number : , agreement type : G, recipient legal name : Joksimovic, Darko (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Watershed flow monitoring data are widely used by municipalities and engineering firms as the bases for_x000D_
designing, sizing, monitoring, controlling, and optimizing drainage and sewer systems. Hence, the correctness_x000D_
and completeness of such time-series plays a significant role in water resources management and engineering_x000D_
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 with_x000D_
sensors malfunctioning or technical issues may result in gaps and irregularities within logged data which will_x000D_
then limit the use of generated datasets. Hence, it is necessity to adapt a reliable and efficient algorithm that can_x000D_
identify data abnormalities and correct faulty datasets. The proposed methodology aims to generate an_x000D_
automated algorithm that by employing statistical and mathematical analysis can identify and flag errors within_x000D_
logged data series. Following that, faulty data will be reconstructed and replaced by means of applicable_x000D_
hydraulic and hydrological equations and or statistical estimation. The partner company, Civica Infrastructure_x000D_
Inc., has developed a powerful web-based, purpose-built data-storage platform named DataCurrent that_x000D_
provides efficient and reliable ways to collect, process and analyze time-series watershed data. Despite_x000D_
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 and_x000D_
environmental gains will bring about significant benefits through automation of flow monitoring data quality_x000D_
assurance and control (QA/QC) processes within the DataCurrent software, reduction of the time and cost_x000D_
associated with manual data correction procedures and enhancement of the quality and reliability of the_x000D_
collected data., program purpose french : Watershed flow monitoring data are widely used by municipalities and engineering firms as the bases for_x000D_
designing, sizing, monitoring, controlling, and optimizing drainage and sewer systems. Hence, the correctness_x000D_
and completeness of such time-series plays a significant role in water resources management and engineering_x000D_
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 with_x000D_
sensors malfunctioning or technical issues may result in gaps and irregularities within logged data which will_x000D_
then limit the use of generated datasets. Hence, it is necessity to adapt a reliable and efficient algorithm that can_x000D_
identify data abnormalities and correct faulty datasets. The proposed methodology aims to generate an_x000D_
automated algorithm that by employing statistical and mathematical analysis can identify and flag errors within_x000D_
logged data series. Following that, faulty data will be reconstructed and replaced by means of applicable_x000D_
hydraulic and hydrological equations and or statistical estimation. The partner company, Civica Infrastructure_x000D_
Inc., has developed a powerful web-based, purpose-built data-storage platform named DataCurrent that_x000D_
provides efficient and reliable ways to collect, process and analyze time-series watershed data. Despite_x000D_
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 and_x000D_
environmental gains will bring about significant benefits through automation of flow monitoring data quality_x000D_
assurance and control (QA/QC) processes within the DataCurrent software, reduction of the time and cost_x000D_
associated with manual data correction procedures and enhancement of the quality and reliability of the_x000D_
collected data., agreement title english : Developing an Automated Reliable Methodology for the Detection and Correction of Error within a Sanitary and Storm Water Data Collection and Processing System, agreement title french : 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 : 25000.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada