Cellular Automata-based high-resolution hydrological modeling for urban digital water information

In this study, we propose and evaluate a Cellular Automata (CA)-based high-resolution hydrological model for an urban digital water information framework. Pluvial flooding in the extreme events and water balance in the non-rainy seasons are usually simulated by different modeling frameworks hampering holistic understandings of the complex water cycle in the urbanized areas. However, for smart water systems such as digital twins or multiverse, street-resolving, high-fidelity water information is required regardless of types of hydrologic events. To provide seamless urban water information on the digital world such as digital twins, Cellular Automata, a rule-based machine learning technique, is adopted and extended to simulate continuous hydrological variables such as inundation depth, infiltration, soil water content, and evapotranspiration in the complex urbanized domain. A proto-type CA model is implemented in the Oncheon-Cheon catchment in Busan, South Korea, which is highly urbanized and vulnerable to pluvial flooding. In the presentation, we discuss advances and challenges in machine learning-based integrated urban water modeling.