A machine learning-based monthly streamflow forecast model for Alberta

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This project will develop a machine-learning (ML) based monthly streamflow forecast tool for two hydrological stations in Alberta: the Oldman River at Lethbridge and the Red Deer River at Dickson Dam. The tool will provide monthly forecasts for March-September (7 predictions per season), with month-by-month updating through incorporating data from the previous month to improve subsequent monthly forecasts. Machine-learning algorithms offer great promise in the field of hydrology, and are increasingly widely-applied for streamflow forecasting, estimation of flow duration curves, rainfall-runoff modelling, and urban water demand forecasting. This project will test the performance of several ML algorithms, including artificial neural networks (ANN), support vector machines (SVM), extreme learning machines (ELM), and radial basis function networks (RBF), for monthly streamflow forecasts. Such forecasts are critical for optimal operation of reservoirs, spring seeding, and water use efficiency improvements, and help to provide early flooding and drought warning. Further, climate change-driven intensification of the hydrological cycle, and its corresponding increase in frequency and magnitude of flood and drought events, makes such forecasting tools increasingly important and valuable. Once the performance of the models is evaluated and deemed to be acceptable, the streamflow forecasts will be postable online by the partner organization, Alberta Environment and Parks, for use by Canadian irrigation districts, municipalities, industries, businesses, and the public downstream of the stations in Alberta. Both the dynamic month-by-month updating of the forecasts and their intended direct application to operations are novel and important characteristics of the research.