Oscillation Flow Dam Operation Method for Algal Bloom Mitigation

Kim, Jungwook; Kwak, Jaewon; Ahn, Jung Min; Kim, Hongtae; Jeon, Jihye; Kim, Kyunghyun

doi:10.3390/w14081315

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Due to river dredging and the construction of multi-function weirs as part of the four major river restoration projects, riverine environments in South Korea (hereinafter Korea) underwent drastic changes within a short period of time [1]. However, they are deteriorating owing to the changes in meteorological and hydraulic conditions due to recent climate change.…”

Section: Introductionmentioning

confidence: 99%

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Kim

et al. 2023

Water

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Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.

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Section: Introductionmentioning

confidence: 99%

Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms

Kim

et al. 2023

Water

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“…In South Korea, reservoir operation for mitigating CyanoHABs has primarily focused on flow control flushing water from a reservoir to a river downstream [22,23]. However, the use of water in reservoirs for improving water quality has not been generally factored into the design of reservoirs in South Korea [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…However, the use of water in reservoirs for improving water quality has not been generally factored into the design of reservoirs in South Korea [24][25][26]. Hence, the flow control using an additional amount of water has been only temporarily taken in order to mitigate CyanoHABs [22], since this flow control can cause water shortage. For a trade-off between the conflicting objectives of improving water quality and alleviating water shortage, optimal operation of reservoirs must be conducted by simultaneously considering both the quantity and quality of water.…”

Section: Introductionmentioning

confidence: 99%

Decision Support Framework for Optimal Reservoir Operation to Mitigate Cyanobacterial Blooms in Rivers

Kim,

Jonoski,

Solomatine

et al. 2023

Sustainability

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Flow control flushing water from reservoirs has been imposed in South Korea for mitigating harmful cyanobacterial blooms (CyanoHABs) in rivers. This measure, however, can cause water shortage in reservoirs, as the measure adopting this flow control may require an additional amount of water which exceeds the water demand allocated to the reservoirs. In terms of sustainability, a trade-off between improving water quality and alleviating water shortage needs to be considered. This study aimed at establishing a practical framework for a decision support system for optimal joint operation of the upstream reservoirs (Andong and Imha) to reduce the frequency of CyanoHABs in the Nakdong River, South Korea. Methodologically, three models were introduced: (1) a machine learning model (accuracy 88%) based on the k-NN (k-Nearest Neighbor) algorithm to predict the occurrence of CyanoHABs at a selected downstream location (the Chilgok Weir located approximately 140 km downstream from the Andong Dam), (2) a multiobjective optimization model employing NSGA-II (Nondominated Sorting Genetic Algorithm II) to determine both the quantity and quality of water released from the reservoirs, and (3) a river water quality model (R2 0.79) using HEC-RAS to simulate the water quality parameter at Chilgok Weir according to given upstream boundary conditions. The applicability of the framework was demonstrated by simulation results using observational data from 2015 to 2019. The simulation results based on the framework confirmed that the frequency of CyanoHABs would be decreased compared with the number of days when CyanoHABs were observed at Chilgok Weir. This framework, with a combination of several models, is a novelty in terms of efficiency, and it can be a part of a solution to the problem of CyanoHABs without using an additional amount of water from a reservoir.

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“…Green algae play an important role in ecosystems as primary producers, but they can cause algal blooms. Reducing algal blooms through dam operation without using additional water resources was modeled with the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model calibrated for the Namhan River, South Korea [18]. The oscillation flow produced a significant variance in flow velocity, leading to a 20-30% reduction in algal bloom density in the Namhan River through the operation of the Chungju Dam [18].…”

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confidence: 99%