Real-time Optimization of Freshwater usage in Aquatic Facilities based on Occupancy and Water Quality data Using Machine Learning Methods.

In this research, occupancy monitoring data, temperature and humidity, and water quality parameters data collected through image processing and Internet of Things (IoT) sensors from multiple swimming pools are going to be processed and analyzed to identify the meaningful relations between these parameters and freshwater usage. The aim is to identify correlations between parameters and formulate the addition of freshwater as a function of number of swimmers, time spent, and activities. In addition, these formulas will be used to optimize freshwater usage while maintaining water quality and health parameters within the standard range defined by health regulations.

Faculty Supervisor:

Fae Azhari

Student:

Hoda Mofidinasrabadi

Partner:

EAIGLE Inc.

Discipline:

Engineering - mechanical

Sector:

Information and cultural industries

University:

University of Toronto