Development and Performance Improvement of a Time-Series-Based AI Model for Flood Forecasting and Warning

ABSTRACT

Floods are a major natural disaster that occurs suddenly, causing loss of life and significant socioeconomic damage, making proactive response through accurate forecasting essential. River water levels are a key indicator for determining flood occurrence, but forecasting accuracy varies depending on hydrological, meteorological, and watershed characteristics. In particular, small-scale rivers have higher water level variability compared to large-scale rivers, limiting their practical flood response capabilities. To overcome these limitations, this study aimed to develop an AI-based river water level forecasting model and improve its forecasting performance. The forecast model is configured to forecast river water levels three hours ahead using time-series data from the previous 24 hours as input, based on the forecast time point. Four locations among existing AI-forecast rivers where flood forecasting is difficult were selected, and input variables reflecting each location's hydrological, meteorological, and oceanographic characteristics were configured. As a result, this study confirmed a trend toward improved flood forecasting performance through the configuration of input variables tailored to each site's characteristics and the adoption of the latest AI models.

Acknowledgments

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).