Machine Learning-Based Decision Support Model for Estimating Search Ranges of Riverine Drowning Accidents Using Flow Rate-Velocity Prediction

This study proposes a machine learning-based decision support model leveraging flow rate-velocity prediction for estimating applicable search ranges in the case of riverine drowning accidents in the primary stream of the Geum River in Korea. This constitutes a new approach that combines a mathematical model, data-driven prediction, and probabilistic factors to overcome the limitations of rescue personnel experience and opinion. As this proposed method yields a more quantified and reasonable search sector than a method dependent on experience, efficient allocation of equipment, manpower, and rescue personnel safety can be expected. Overall, these findings contribute to the decision-making model based on data obtained when searching for missing individuals in the river. Future work should expand model validation to other major rivers in Korea, such as the Han-gang and Nark-dong-gang Rivers. This will enable positive applications of such methods at river rescue sites.