Integrated Machine Learning and Hydrodynamic Modeling for Agricultural Land Flood Under Climate Change Scenarios

ABSTRACT Floods can cause significant damage to land, infrastructure, and individual well‐being. In the Canadian prairies, flood is a recurring natural disaster for farmers and ranchers. The flat terrain and extensive agricultural lands make the region vulnerable to flooding. Climate change could alter hydrological processes, leading to an increase in both frequency and intensity of flood events. In this study, machine learning and hydrodynamic models were combined to predict flood risks on agricultural lands based on various possible climate change scenarios. For this research, outputs from CanESM2, SDSM, ANN, HEC‐GEORAS, and HEC‐RAS were integrated to generate 2D flood simulation outputs. Climate change models CanESM2 and SDSM were used to simulate the possible future temperature and precipitation regimes (RCP 8.5 and RCP 4.5). The Artificial Neutral Network (ANN) model was used to predict possible future snowfall levels based on simulated precipitation and ambient air temperature regimes. The second ANN was further trained with first ANN data to predict possible flow rates in the river. A flood‐frequency analysis was conducted using 10, 50, and 100 years flood return periods. The collective data output was used in HEC‐RAS to simulate flooding under respective return periods. The georeferenced vector and raster data were generated using ArcGIS and HEC‐GEORAS. Comparative flood simulation outputs were generated using historical data. The flood simulation results using historical data were compared to climate change conditions. The results indicate that climate change could potentially exacerbate the severity of floods in agricultural lands across the prairies. The greater return periods correspond to greater flood depths, velocities, and inundation areas, with RCP 8.5 creating the most extreme conditions. In addition, climate change could potentially accelerate peak flows in the river and increase hydrological pressure.