| Title |
Simulating of Harmful Cyanobacteria in the Middle of Nakdong River of a 3-D Hydrodynamic and Water Quality Model With Genetic Algorithm |
| Authors |
전유경(Yu Kyeong Jeon) ; 이혜원(Hye Won Lee) ; 최정현(Jung Hyun Choi) |
| DOI |
https://doi.org/10.15681/KSWE.2025.41.3.163 |
| Keywords |
Environmental Fluid Dynamics Code (EFDC); Genetic Algorithm (GA); Harmful Algal Blooms (HABs); Nakdong River; Parameter optimization |
| Abstract |
Harmful cyanobacteria are the main cause of harmful algal blooms (HABs) and have a negative impact on water quality and public health by inducing bad taste and toxic substances. Thus, it is necessary to develop an algae prediction model, especially for harmful cyanobacteria, with reliable accuracy to establish an effective water quality management strategy for HABs. Since the accuracy of algae prediction models is greatly affected by the parameter settings, it is crucial to estimate appropriate parameters counting on the algal communities of target area. In this study, an environmental fluid dynamics code model was applied to the middle of the Nakdong River. Parameter optimization based on a genetic algorithm was utilized to estimate the optimal parameters reflecting the characteristics of variable algal groups in the target areas. The optimal parameter values for different algal groups differed depending on the algal occurrence pattern and dominant algal species. Model calibration was conducted using the 2022 observed data. It properly reproduced the time-series patterns of water elevation, water temperature, DO, TOC, TN, NH4-N, NO3-N, TP, PO4-P, Chl-a, and harmful cyanobacteria in the study area. The reliability of the model was verified by simulating the number of harmful cyanobacteria at the algae alert system station, Gangjeong-Goryeong. The observed and simulated values ??showed similar levels. The study results are expected to improve the reliability of model prediction by applying parameter optimization, reflecting the characteristics of variable algal groups of the target areas and providing scientific evidence for establishing preemptive algae management. |