The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction.

This study aimed to map the 2022 flood with a 16.5-year return period near a bridge on the Red River, close to Grafton City, North Dakota, and evaluate the scour potential around the bridge. The Red River Basin (RRB) near Grand Forks, ND, and Emerson, ND, is a cold region river vulnerable to floods. Local scouring around bridge piers during floods can lead to hydraulic structure failure. An Autonomous Surface Vehicle (ASV) equipped with LiDAR DEM data from the ND DWR’s LiDAR dataset was used to collect comprehensive bathymetry and discharge data, including the 2022 flood. The HEC-RAS model was used to create flood maps, and the Colorado State University (CSU) methodology was employed to assess local scour around the bridge pier. The study area recorded maximum velocities of 1.71 m/s, 1.87 m/s, and 1.56 m/s for discharge values of 368 m³/s, 784 m³/s, and 1335 m³/s, respectively, with higher velocities recorded upstream of the bridge. The maximum water depth reached 13.14 m during the peak discharge of 1335 m³/s. Higher discharge resulted in increased Froude number and contraction scour depth, with the latter continuing to increase even when the Froude number decreased as water reached the bridge deck. The study highlights the effectiveness of integrating ASVs, bathymetry, and LiDAR data to comprehensively understand flood dynamics and bridge scour in cold region rivers, offering the way for the development of effective flood control measures and strategies to safeguard critical infrastructure.