Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Given flood risks, forecasting river flood is important for water resource management and risk preven-tion.x000D
Even though long-term flood forecasting is a difficult task, log-term flood forecasting is very useful forx000D
instance to let municipalities to have enough time for preparation and action. The efforts in ultimately creatingx000D
a long-term forecasting framework are usually faced with the challenges stemming from weather dynamics.x000D
Machine learning techniques have recently been recognized and widely adopted for modeling complexx000D
problems in sustainable infrastructures, especially in forecasting extreme events. In particular, machinex000D
learning techniques have been successfully applied for flood forecasting and provided improved forecastingx000D
techniques and relatively more accurate results. More recently, Ensemble learning has re-ceived a significantx000D
amount of interest. Ensemble learning provides a more stable prediction performance compared to singlex000D
model, driving the diminishing uncertainty behaviour of ensemble learning. The main goal of this researchx000D
project is to develop ensemble based machine learning (EML) models for the long-term forecasting of riverx000D
flow under different information criterion and limited history of extreme events. The new, highly accurate andx000D
reliable long-term forecasting models will provide US, and in turn their clients across Canada, with very usefulx000D
models that will allow significantly improved long-term forecast-ing and will consequently help in effectivelyx000D
and sustainably plan and manage extreme events response strategies.