Estimation of Dual Permeability Model Parameters in Agricultural Fields using a Genetic Algorithm-Based Inverse Modeling Framework: Reference code

This repository contains the reference code associated with the research article on estimating dual permeability model (DPM) parameters in agricultural fields using a genetic algorithm (GA)-based optimization framework. The study integrates Hydrus-1D for hydrological simulations and employs PyGAD, an open-source Python library, for optimization. The provided code automates the modification of Hydrus-1D input files, executes simulations, processes output data, and optimizes soil hydraulic parameters using genetic algorithms. The workflow involves: Setting up the Hydrus-1D simulation files Modifying the SELECTOR.IN file dynamically Executing the Hydrus-1D model Extracting and analyzing simulated soil moisture data Implementing a genetic algorithm-based optimization to match simulated and observed field data The code has been tested on several type of datasets and boundary condition and works well.