Reference number : GC-2017-Q3-00478, amendment number : , agreement type : G, recipient legal name : Chebana, Fateh (Institut national de la recherche scientifique), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : 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 for_x000D_
instance to let municipalities to have enough time for preparation and action. The efforts in ultimately creating_x000D_
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 complex_x000D_
problems in sustainable infrastructures, especially in forecasting extreme events. In particular, machine_x000D_
learning techniques have been successfully applied for flood forecasting and provided improved forecasting_x000D_
techniques and relatively more accurate results. More recently, Ensemble learning has re-ceived a significant_x000D_
amount of interest. Ensemble learning provides a more stable prediction performance compared to single_x000D_
model, driving the diminishing uncertainty behaviour of ensemble learning. The main goal of this research_x000D_
project is to develop ensemble based machine learning (EML) models for the long-term forecasting of river_x000D_
flow under different information criterion and limited history of extreme events. The new, highly accurate and_x000D_
reliable long-term forecasting models will provide US, and in turn their clients across Canada, with very useful_x000D_
models that will allow significantly improved long-term forecast-ing and will consequently help in effectively_x000D_
and sustainably plan and manage extreme events response strategies., program purpose french : 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 for_x000D_
instance to let municipalities to have enough time for preparation and action. The efforts in ultimately creating_x000D_
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 complex_x000D_
problems in sustainable infrastructures, especially in forecasting extreme events. In particular, machine_x000D_
learning techniques have been successfully applied for flood forecasting and provided improved forecasting_x000D_
techniques and relatively more accurate results. More recently, Ensemble learning has re-ceived a significant_x000D_
amount of interest. Ensemble learning provides a more stable prediction performance compared to single_x000D_
model, driving the diminishing uncertainty behaviour of ensemble learning. The main goal of this research_x000D_
project is to develop ensemble based machine learning (EML) models for the long-term forecasting of river_x000D_
flow under different information criterion and limited history of extreme events. The new, highly accurate and_x000D_
reliable long-term forecasting models will provide US, and in turn their clients across Canada, with very useful_x000D_
models that will allow significantly improved long-term forecast-ing and will consequently help in effectively_x000D_
and sustainably plan and manage extreme events response strategies., agreement title english : An ensemble learning framework for long-term flood forecasting, agreement title french : An ensemble learning framework for long-term flood forecasting, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-11-08, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada