Integrated Approach to Flood Simulation Using MATLAB, EnKF Data Assimilation, and Perturbation Sensitivity Analysis

Flood forecasting and flood control are crucial components of disaster risk reduction and prevention, particularly in areas where hydrological disasters pose significant risks. This paper presents an integrated approach that includes flood modeling using MATLAB, data assimilation through the Ensemble Kalman Filter (EnKF), and perturbation sensitivity analysis to enhance flood prediction data. The primary objective is to establish a unified MATLAB environment that facilitates simulation modeling, data assimilation, and sensitivity analysis, thereby enhancing the resilience of predictions against uncertainties.Hydrodynamic modeling, based on MATLAB computational tools, is utilized for flood simulation, rainfall runoff, and flood propagation. The EnKF is employed within MATLAB scripts to update real-time observations to simulation predictions, minimizing prediction errors. Furthermore, perturbation sensitivity analysis, using methods such as Monte Carlo simulation and Sobol analysis, identifies which model variables have the greatest influence, thereby improving the model's reliability.Results indicate that the flood prediction accuracy is improved by employing EnKF in conjunction with simulation models, compared to using EnKF alone. This combination is characterized by a reduction in prediction error and a closer match to observed data. Sensitivity analysis also enables the identification of other factors affecting the model, thus offering opportunities for further improvement. The integrated MATLAB framework is demonstrated to be a flexible and effective system for flood forecasting, adaptable to various conditions and datasets.The limitation of this study is to emphasize the importance of the synergistic effects of simulation, data assimilation, and sensitivity analysis in enhancing the understanding of flood prediction. The framework will be further developed and applied to other hydrological systems..