A machine-learning tool developed by Mohamed Almetwally Ahmed and Sam Li provides accurate predictions for flood-prone areas.
A machine-learning tool developed by Mohamed Almetwally Ahmed and Sam Li provides accurate predictions for flood-prone areas.
As recent flooding in Spain and elsewhere revealed, every minute of warning given to people ahead of a possible flood can save lives and property. A new paper in the journal Hydrology may help authorities improve flood evacuation protocols with help from a machine-learning model developed by Concordia researchers.
PhD candidate Mohamed Almetwally Ahmed and Samuel Li, professor and chair of the Department of Building, Civil and Environmental Engineering, created a method that uses artificial intelligence to more accurately predict short-term river discharge.
Using historical data and a novel set of weather-based predictors, the authors based their research on measuring advection — the rate of water movement — between two hydrometric stations on the Ottawa River. A test case was created using two stations roughly 30 kilometres apart. The downstream station had been deactivated for many years and the upstream station was still active.
Read more at Concordia University
Image: Samuel Li and Mohamed Ahmed (Credit: Concordia University)
