Predicting Cyanobacterial Harmful Algal Blooms (CyanoHABs) in a Regulated River Using a Revised EFDC Model

Jonoski

Solomatine

2022

Water

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

Section: Study Areamentioning

confidence: 99%

“…Cyanobacterial blooms appear when phytoplankton proliferates massively in lentic water such as lakes, reservoirs, or ponds, owing to eutrophication and stratification [2,14]. In other words, an increase in the nutrients and a rise in the water temperature of stagnant water bodies can bring about cyanobacterial blooms [15].…”

Section: Introductionmentioning

confidence: 99%

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Jonoski

Solomatine

2022

Water

“…Phytoplankton is divided into three colonies (diatoms, green algae, and cyanobacteria) that consider seasonal transitions. However, since algae groups with different occurrence and behavioral characteristics coexist in the same colony, the conventional EFDC model has limitations in reproducing the rapid occurrence and complex species transition of algae [37]. Microcystis, a cyanobacteria, causes harmful algal blooms in summer in Korea.…”

Section: Application Of Equations For General Water Qualitymentioning

confidence: 99%

Oscillation Flow Dam Operation Method for Algal Bloom Mitigation

Kwak²,

Ahn

et al. 2022

Water

Self Cite

Green algae play an important role in ecosystems as primary producers, but they can cause algal blooms, which are socio-environmental burdens as responding to them requires water resources from dam reservoirs. This study proposes an alternative for reducing algal blooms through dam operation without using additional water resources. A novel oscillation flow concept was suggested: oscillating discharge of dam for irregular flow. To examine its effect, the Environmental Fluid Dynamics Code—National Institute of Environment Research (EFDC-NIER) model was constructed and calibrated for the Namhan River, South Korea, from downstream of the Chungju Dam to downstream of Gangcheon Weir. The water quality in the study area were simulated and analyzed for August 2019, which is when the largest number of harmful cyanobacteria had been reported in recent years. Our results showed that the oscillation flow produced significant variance of flow velocity, and algal bloom density in the Namhan River was reduced by 20–30% through the operation of the Chungju Dam. However, further study and investigation are required before practical application.

“…Based on the EFDC_DS (20100328 version), the National Institute of Environmental Research (NIER) in South Korea has added more features, such as the operating function of hydraulic structures in the major rivers of South Korea; a simulation function of multialgae species; a vertical migration mechanism for cyanobacteria; akinete generation and germination mechanisms; and a mechanism for the effect of salt and toxicity on freshwater and sea algae, wind stress, and benthic flux of inorganic nutrients according to changes in the oxidation/reduction conditions [26].…”

Section: Introductionmentioning

confidence: 99%

“…Ahn et al [26,27] established a method for short-term prediction of algae by using an improved source code with an operating function of hydraulic river structures and of mechanisms for vertical migration of cyanobacteria and akinete creation and germination. They also proposed an optimal method for predicting algae by applying hyperspectral remote-sensing data to the EFDC-NIER model.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of a 3-Dimensional Hydrodynamics Model Using a Hybrid Method with MPI and OpenMP

Ahn

Cho³

et al. 2021

Processes

Self Cite

Process-based numerical models developed to perform hydraulic/hydrologic/water quality analysis of watersheds and rivers have become highly sophisticated, with a corresponding increase in their computation time. However, for incidents such as water pollution, rapid analysis and decision-making are critical. This paper proposes an optimized parallelization scheme to reduce the computation time of the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model, which has been continuously developed for water pollution or algal bloom prediction in rivers. An existing source code and a parallel computational code with open multi-processing (OpenMP) and a message passing interface (MPI) were optimized, and their computation times compared. Subsequently, the simulation results for the existing EFDC model and the model with the parallel computation code were compared. Furthermore, the optimal parallel combination for hybrid parallel computation was evaluated by comparing the simulation time based on the number of cores and threads. When code parallelization was applied, the performance improved by a factor of approximately five compared to the existing source code. Thus, if the parallel computational source code applied in this study is used, urgent decision-making will be easier for events such as water pollution incidents.