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In recent years, machine learning models, particularly Long Short-Term Memory (LSTM), have proven to be effective alternatives for rainfall-runoff modeling, surpassing traditional hydrological modeling approaches 1. These models have predominantly been implemented and evaluated for rainfall-runoff simulations. However, operational hydrology often requires short-and mid-term forecasts. To be effective, such forecasts must consider past observed values of the predicted variables, requiring a data assimilation procedure 2, 3, 4. This presentation will evaluate several approaches based on the combination of open-source machine learning tools and data assimilation strategies for short-and mid-term discharge forecasting of flood and/or drought events. The evaluation is based on the rich and well-documented CAMELS dataset 5, 6, 7. The tested approaches include:(1) coupling pre-trained LSTMs on the CAMELS database with a Multilayer Perceptron (MLP) for prediction error corrections,(2) direct discharge MLP forecasting models specific for each lead time, including past observed discharges as input variables, and (3) option 2, including the LSTM-predicted discharges as input variables. In the absence of historical archives of weather forecasts (rainfall, temperatures, etc.), the different forecasting approaches will be tested in two configurations:(1) weather forecasts assumed to be perfect (using observed meteorological variables over the forecast horizon in place of predicted variables or ensembles) and (2) use of ensembles reflecting climatological variability over the forecast horizons for meteorological variables ensembles made up of time …