Reference number : GC-2017-Q4-00331, amendment number : , agreement type : G, recipient legal name : Chen, Shengyuan (York University)|Chen, Shengyuan (Université York), 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 : Drinking water poisoning are not rare even in well developed countries like USA and Canada. Pollutants like_x000D_
CaCO3, Sodium Chloride, Lead, K12 E.Coli, Fungal could enter our water system in every stage, water plant,_x000D_
pipe, or the end faucet. Hence it is not enough to monitor water quality only at the source. Historically it is_x000D_
impossible or prohibitively expensive to install monitoring devices at the end user side. Only big entities like_x000D_
hospitals, food processing companies, etc. can afford water quality monitoring devices. Now the development_x000D_
of sensor technology makes it possible and economical to install sensors at each individual faucet. However,_x000D_
sensors at individual faucets are subject to great unknown factors and noises. After all, a kitchen is not a_x000D_
spotless scientific lab. This causes greater difficulties in classifying whether or not the drinking water under_x000D_
monitoring is polluted or not. We need a new robust machine learning model and algorithm, which can_x000D_
accurately trigger alarms such a noisy environment. The advancement of big data technologies and machine_x000D_
learning algorithms make this otherwise impossible goal highly likely., program purpose french : Drinking water poisoning are not rare even in well developed countries like USA and Canada. Pollutants like_x000D_
CaCO3, Sodium Chloride, Lead, K12 E.Coli, Fungal could enter our water system in every stage, water plant,_x000D_
pipe, or the end faucet. Hence it is not enough to monitor water quality only at the source. Historically it is_x000D_
impossible or prohibitively expensive to install monitoring devices at the end user side. Only big entities like_x000D_
hospitals, food processing companies, etc. can afford water quality monitoring devices. Now the development_x000D_
of sensor technology makes it possible and economical to install sensors at each individual faucet. However,_x000D_
sensors at individual faucets are subject to great unknown factors and noises. After all, a kitchen is not a_x000D_
spotless scientific lab. This causes greater difficulties in classifying whether or not the drinking water under_x000D_
monitoring is polluted or not. We need a new robust machine learning model and algorithm, which can_x000D_
accurately trigger alarms such a noisy environment. The advancement of big data technologies and machine_x000D_
learning algorithms make this otherwise impossible goal highly likely., agreement title english : Machine Learning Models for Drinking Water Quality Monitoring based on Sensor Data, agreement title french : Machine Learning Models for Drinking Water Quality Monitoring based on Sensor Data, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, 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