Flood and drought prediction, simulation, and monitoring for the City of Terrebonne

The increasing frequency of flooding has driven research to improve near real-time flood mapping from remote-sensing data. In Quebec, in
the spring of 2017, several regions experienced severe flooding caused by consecutive record-setting rain events during snowsmelt from early
April to mi0-d-May. The current project aims to provide real-time monitoring tools not only for flooding but for drought as well, i.e.,
visualization and simulation tools using both remote sensing data, but also data collected from an Internet of Things network. In addition, the
project includes the design and development of prediction tools for both flood and drought, using machine learning algorithms.
The project is a multidisciplinary one, with expertise in visualization/simulation, prediction of flood/drought with machine learning, and
networking expertise for the efficient, reliable and safe transmission of data.

Faculty Supervisor:

Brigitte Jaumard;Charalambos (Charis) Poullis

Student:

Nima Sarang

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and cultural industries

University:

Concordia University