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- [ONLINE] H11K-VR8945: Enhancing Hydrological Model Calibration Using Particle Swarm Optimization and Genetic Algorithm in the Duck River Basin, USA
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Saptaporna Dey, Bangladesh University of Engineering and Technology (First Author, Presenting Author)
Afnan Sami Arian, Bangladesh University of Engineering and Technology
Arjun Deb Ricky, Bangladesh University of Engineering and Technology
Kazi Ashfaque Hossain, Bangladesh University of Engineering and Technology
Sujoy Dey, Bangladesh University of Engineering and Technology
Exactly predicting how rainfall becomes flow in rivers is important for predicting floods and managing water resources. But it's often difficult and time-consuming to develop these predictions with computer models since so many settings have to be adjusted by trial and error. This study used two advanced techniques, Particle Swarm Optimization and Genetic Algorithm, to improve how these settings are chosen within a popular hydrological model called HEC-HMS. The study was tested with the Duck River Basin, Tennessee, USA. The techniques facilitated improved accuracy of previous floods and daily flow within rivers by automatically finding better settings. The study compared old and new approaches, which showed that new approaches achieved better results, especially where multiple goals were used to achieve improvement. Using a large number, however, made the process slower and used up more computer power. Overall, it was found that automatic techniques are capable of boosting the accuracy and speed of models predicting floods, a consideration worth taking by water planners and communities who utilize these predictions.
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