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Such tools are useful to inform local and region-specific water management and flood forecasting related activities. Two powerful deep-learning variants of the Recurrent Neural Network were investigated, namely the standard and attention-based encoder-decoder long short-term memory (LSTM) models. Both models were forced with past hydro-meteorological states and daily meteorological data with a look-back time window of several days. These models were tested for 10 different watersheds from the Ottawa River watershed, located within the Great Lakes Saint-Lawrence region of Canada, an economic powerhouse of the country. The results of training and testing phases suggest that both models are able to simulate overall hydrograph patterns well when compared to observational records. Between the two models, the attention model significantly outperforms the standard model in all watersheds, suggesting the importance and usefulness of the attention mechanism in ML architectures, not well explored for hydrological applications. The mean performance accuracy of the attention model on unseen data, when assessed in terms of mean Nash\u2013Sutcliffe Efficiency and Kling-Gupta Efficiency is, respectively, found to be 0.985 and 0.954 for these watersheds. Streamflow forecasts with lead times of up to 5\u00a0days with the attention model demonstrate overall skillful performance with well above the benchmark accuracy of 70%. The results of the study suggest that the encoder\u2013decoder LSTM, with attention mechanism, is a powerful modelling choice for developing streamflow forecasting systems for Canadian watersheds.<\/jats:p>","DOI":"10.1007\/s00521-022-07523-8","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T23:05:07Z","timestamp":1657753507000},"page":"19995-20015","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism"],"prefix":"10.1007","volume":"34","author":[{"given":"Lakshika","family":"Girihagama","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0451-7480","authenticated-orcid":false,"given":"Muhammad","family":"Naveed Khaliq","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Lamontagne","sequence":"additional","affiliation":[]},{"given":"John","family":"Perdikaris","sequence":"additional","affiliation":[]},{"given":"Ren\u00e9","family":"Roy","sequence":"additional","affiliation":[]},{"given":"Laxmi","family":"Sushama","sequence":"additional","affiliation":[]},{"given":"Amin","family":"Elshorbagy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"7523_CR1","unstructured":"Rockwood DM (1964) Streamflow synthesis and reservoir regulation. 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