Affiliation:
1. Departamento de Informática, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Leganés, Spain
2. Departamento de Biología Marina, Universidad de Panamá, Estafeta Universitaria, Panamá 4 P.O. Box 3366, Panama
Abstract
Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.
Reference114 articles.
1. (2022, July 19). FloodList Database. Available online: https://floodlist.com/.
2. Eckstein, D., Künzel, V., and Schäfer, L. (2021). Global Climate Risk Index 2021: Who Suffers Most from Extreme Weather Events, Germanwatch.
3. Adikari, Y., and Yoshitani, J. (2009). Global Trends in Water-Related Disasters: An Insight for Policymakers, International Centre for Water Hazard and Risk Management (ICHARM). World Water Assessment Programme Side Publication Series, Insights.
4. Water-related disaster risk reduction: Time for preventive action! Position paper of the High Level Experts and Leaders Panel (HELP) on water and disasters;Wieriks;Water Policy,2015
5. A review on hydrological models;Devia;Aquat. Procedia,2015