Code and Data Supporting the Manuscript: Interpretable Genetic Programming for Flood Hydrograph Simulation and River Water-Level Prediction: A Comparative Machine Learning Study

This repository provides research data, saved model results, and MATLAB and Python scripts supporting the manuscript “Interpretable Genetic Programming for Flood Hydrograph Simulation and River Water-Level Prediction: A Comparative Machine Learning Study.” The archive contains two components: GP_Scenario_Comparison_18_Models: Eighteen saved Genetic Programming configurations covering normal conditions and extreme water-level changes of at least 20 and 40 cm, with different GP operator sets and random or chronological data splitting. The package includes saved models, performance metrics, symbolic equations, predictions, and figures. Final_5Model_Comparison: Six flood events and saved outputs for GP-4, GP-7, Random Forest, ANFIS, and XGBoost. MATLAB and Python scripts reconstruct predictions and generate comparative performance metrics and flood hydrograph figures. README files provide instructions for using the datasets and reproducing the supplied analyses without retraining the models.