Implementation of heuristic search algorithms in the calibration of a river hydraulic model

• Published in: Environmental modelling & software : with environment data news Vol. 157; p. 105537
• Main Author: Hosseiny, Hossein
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022
• Subjects: 2-Dimensional flood hydraulic model; algorithms; calibration; computer software; Genetic algorithm; Green River; neural networks; Particle swarm optimization; rivers; roughness; Utah; Green River; Utah; Genetic algorithm; Particle swarm optimization; 2-Dimensional flood hydraulic model
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2022.105537

In this contribution, two heuristic search algorithms, (1) particle swarm optimization (PSO) and (2) genetic algorithm (GA) are used to calibrate the iRIC, a two-dimensional hydraulic model for a test case on the Green River in Utah. The goal of the search algorithms was to find the combination of roughness values that minimized model error compared to the measured water surface elevation. An artificial neural network (ANN), which was trained based on the simulated water surface profiles obtained from iRIC, was used to measure the fitness of each solution. Implementation of PSO and GA both improved the accuracy of the calibration by 34 and 30 percent respectively relative to the trial and error. This research demonstrates that the implementation of heuristic search models provides an objective methodology for the calibration of hydraulic models with improved performance when compared to models calibrated via trial and error. •Heuristic search algorithms provide an objective method for hydraulic model calibration.•Particle swarm optimization (PSO) and genetic algorithm (GA) improved the accuracy of model calibration by 34 and 30 percent respectively.•PSO found the optimum parameters for model calibration 28 folds faster than the GA.