Development of flood forecast early warning systems is critical as storm and spring melt events intensify due to climate change. Predicting these events with precision and adequate warning time presents a significant technological and computational challenge for managers of water resource systems.

Effective flood forecasting and warning systems combine several complex modelling tools. Climate forecast, hydrologic and hydraulic models are used together to forecast flows, water levels and flood inundation extents. Such models are computationally expensive, and each has associated uncertainties that must be quantified.

This project addresses these issues by coupling models with Machine Learning algorithms and high-performance computing infrastructures. The approach will enable the existing flood forecasting platform (known as ISWMS) to better i) quantify uncertainties associated with flood forecasts and ii) minimize run-time of the computationally expensive models embedded within the forecasting system.

Industry Partner(s):Greenland International Consulting Inc.

Academic Institution:University of Guelph

Academic Researcher: Prasad Daggupati

Focus Areas: Environment & Climate

Platforms: Cloud