Application of the LSTM model for streamflow forecasting in the Great-Lakes region

Application of the LSTM model for streamflow forecasting in the Great-Lakes region | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 April 2026 V1 Latest version Share on Application of the LSTM model for streamflow forecasting in the Great-Lakes region Authors : Étienne Gaborit 0000-0002-9787-9124 [email protected] , Gabriel Sasseville 0000-0001-8845-2025 , Vincent Fortin 0000-0002-2145-4592 , and Milena Dimitrijevic Authors Info & Affiliations https://doi.org/10.22541/au.177507105.59575168/v1 77 views 41 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Long Short-Term Memory (LSTM) model is a deep learning method that has proven very competitive with regard to streamflow predictions over recent years. In contrast with basic recurrent neural networks, the LSTM’s memory line allows the model to retain information over long periods of time, solving the vanishing gradient problem. In this work, we applied the LSTM model in the region of the Great Lakes, first in a “hindcast” mode when fed with dynamic forcings coming from the Canadian Surface Reanalysis version 3.2 (CaSR v3.2) produced at Environment and Climate Change Canada (ECCC), and then in a “forecasting” mode when simply replacing the last days of the 365-days lookback window of the data cube provided to the LSTM model with actual forecasts from ECCC’s Global Deterministic Prediction System (GDPS). To do so, the model was first trained over 2001-2018 and with a set of 212 streamflow gauges in the Great-Lakes region, and tested over 2019-2023, when using only CaSR 3.2 as the source of the dynamic forcings needed by the model to evaluate its temporal robustness. Then, the model was applied over a full hydrologic year spanning over 2021/2022 in a forecast mode, producing streamflow forecasts up to 6 days. These LSTM forecasts are compared to the streamflow forecasts that were performed with the National Surface and River Prediction System, which consists of ECCC’s distributed and physically-based hydrologic forecasting system. While the LSTM already shows very promising results when compared to NSRPS forecasts, there is still room for improving the LSTM forecasts and combining the strengths of both systems, as well as further work needed to prepare the LSTM model for operational deployment at ECCC. Supplementary Material File (agu_2025_lstm_poster_eg.pdf) Download 3.56 MB Information & Authors Information Version history V1 Version 1 01 April 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords forecasting hydrology machine learning Authors Affiliations Étienne Gaborit 0000-0002-9787-9124 [email protected] Environment and Climate Change Canada View all articles by this author Gabriel Sasseville 0000-0001-8845-2025 University of Montreal View all articles by this author Vincent Fortin 0000-0002-2145-4592 Environment and Climate Change Canada View all articles by this author Milena Dimitrijevic Environment and Climate Change Canada View all articles by this author Metrics & Citations Metrics Article Usage 77 views 41 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Étienne Gaborit, Gabriel Sasseville, Vincent Fortin, et al. Application of the LSTM model for streamflow forecasting in the Great-Lakes region. Authorea . 01 April 2026. DOI: https://doi.org/10.22541/au.177507105.59575168/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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