Visualizing the Results of the Calibration of the Distributed Hydrological Model Iber+ with the Surrogate-Assisted Evolutionary Algorithm

This data represents outcomes from experiments focused on optimizing a distributed hydrological model via a Surrogate-Assisted Evolutionary Algorithm (SAEA). Two scripts perform data analysis, visualization, and strategy comparison. The first script imports pertinent datasets, visualizes the model's performance, and applies the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to create a confidence band for predictions. The second script manages data for multiple generations and compares Monte Carlo (MC) and Evolutionary Algorithm with Surrogate Modeling (EA-SM) strategies, visualized in a scatter plot. Together, these scripts provide comprehensive insights into the model's performance, efficiency, and potential enhancements.