Enhanced flood prediction using LSTM and climate parameters: multi-station analysis of snowmelt-induced flooding in the Red River of the North

ABSTRACT Flooding in cold regions, particularly driven by snowmelt and climate variability, presents complex challenges for accurate prediction and effective risk management. The current study aims to bridge these gaps by integrating all three components – cold region river systems, long short-term memory (LSTM) modeling, and a broader set of climate variables. Specifically, we incorporate additional climate parameters, including air temperature, humidity, solar radiation, and wind speed, into the LSTM model. Using 12 years of hourly water level data (2007–2019) from three USGS stations along the Red River – Pembina, Drayton, and Grand Forks – the model predicts water levels at lead times of 6, 12 h, 1 day, 3 days, and 1 week. The incorporation of climate variables significantly improved short-term prediction accuracy, achieving R2 values of 0.999 for 6-h forecasts across all stations, demonstrating the potential for real-time flood warning systems. For 1-week predictions, R2 values were 0.778 for Grand Forks, 0.816 for Drayton, and 0.864 for Pembina, reflecting a decrease in accuracy with longer prediction horizons. These findings highlight the effectiveness of LSTM models for short-term flood forecasting in snowmelt-prone regions and underscore the need for further refinement to address long-term hydrological forecasting challenges, particularly under variable climatic conditions.