Application of Global Predictive Real Time Control Strategy to Improve Flooding Prevention Performance of Stormwater Basins

Sustainability as one of the key elements of Smart cities, can be realized by employing Real Time Control Strategies for city’s infrastructures. Nowadays Stormwater management systems play an important role in mitigating the impacts of urbanization on natural hydrological cycle. Besides, these systems have a huge potential to be managed in such a way that they would be adaptable to global challenges like climate change. Hence, a dynamically managed system that can adapt itself to instability of the environmental conditions is desirable. Some Global Predictive Real Time Control algorithms will be developed in this project to optimize the performance an integrated system of stormwater management basins in terms of flooding prevention. Then a Hybrid metaheuristic approach from the combination of Artificial Neural Network (ANN) and Genetic algorithm (GA) will be employed to achieve a sufficiently good solution to our optimization problem. At the end, an improved performance at system-level for the stormwater basins is expected which will result in much less flooding in downstream urban areas in comparison to static managed basins.

Faculty Supervisor:

Sophie Duchesne

Student:

Partner:

University of Hawaii

Discipline:

Engineering

Sector:

Environmental Science and Technology; Sustainability & the Environment; Natural Resources

University:

Université du Québec : Institut national de la recherche scientifique

Program: