Reference number : 172-2021-2022-Q2-978108, amendment number : 1, agreement type : G, recipient legal name : The Royal Institution for the Advancement of Learning/McGill University, recipient country : CA, recipient province : QC, recipient city : Montreal|Montréal, recipient postal code : H3A 2M7, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Seasonal prediction of ice conditions in the St. Lawrence Seaway using AI, agreement title french : Prévisions saisonnières de l’état de la glace sur la voie maritime du Saint-Laurent à l’aide de l’intelligence artificielle, agreement number : 978108, agreement value : 290750.0, agreement start date : 2021-09-01, agreement end date : 2025-08-31, description english : Knowledge of ice conditions from freeze-up to break-up is critical for hydrological forecasting and monitoring, as well as for safe operation of ships in icy waters. However, forecasts of ice conditions are almost nonexistent for the St-Lawrence Seaway, a major shipping channel in Canada. This project will develop a forecast model for the (sub-) seasonal evolution of ice conditions in the St-Lawrence River based on Machine Learning. The purpose of these predictions is to aid stakeholders in decision-making and risk analysis for their activities. Ideally, the forecasts will have a lead-time of 30 to 60 days and will be updated daily. First, the model will focus on forecasting ice conditions in the St-Lawrence Seaway, since there is currently a lack of ice forecasts in this region. The model will then be extended to the Estuary/Gulf of the St-Lawrence River if the forecasts for the Seaway are shown to be skillful., description french : Prévisions saisonnières de l’état de la glace sur la voie maritime du Saint-Laurent à l’aide de l’intelligence artificielle, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : This agreement has been amended 1 time(s). The end date of this agreement has been modified by 365 days., additional information french : Cet accord a été modifié 1 fois.  La date de fin du présent accord a été changé de 365 jours., owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada