Retrieval and monitoring of water quality parameters based on hyperspectral remote sensing data and intelligent algorithms

The research will develop an AI model driven by remote sensing data from a satellite, to identify the spatial variability of water pollutants present in lake water, to improve the understanding of the potential for algal blooms. With the development of the AI model, the use of remote sensing will be enabled to predict the potential for algal blooms in lakes that can then be used as an early warning system for water quality which will enable water intakes to water treatment plants can better plan their operating horizons to include, as an example, shutting down the intake system, to protect the integrity of the water treatment plant, and improve the quality of water provided to water consumers.

Faculty Supervisor:

Ed McBean

Student:

Yu Li

Partner:

CanadaWTX Inc.

Discipline:

Engineering - other

Sector:

University:

University of Guelph

Program: