JK
J. Kim
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1
The World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA) provide guidelines on the maximum levels of nitrate nitrogen (NO3-N) contained in drinking water since excess nitrate ingestion may harm human health. Thus, monitoring and controlling the NO3-N concentration is of paramount importance, especially in sources of drinking water such as the Nakdong River in South Korea. This study addresses NO3-N pollution in the Nakdong River in South Korea, where such pollution mostly comes from diffuse sources in the catchment due to the agricultural use of fertilizers. The objective of this study is to suggest guidelines for designing strategies to control NO3-N in this river using a process-based model developed with HEC-RAS. The model was built based on water quality parameters (water temperature, dissolved oxygen, ammonia nitrogen, etc.) related to NO3-N dynamics incorporating hydraulic and meteorological data. This model simulated NO3-N dynamics downstream under 55 scenarios while focusing on a section near locations of drinking water intakes. The scenarios were constructed based on variations in water quantity and quality upstream. The simulation results showed that the peak concentration of NO3-N downstream could be directly controlled by limiting the NO3-N concentration upstream. Additionally, control of the flow rate upstream could also lead to a reduction in the overall average concentration of NO3-N downstream, but this predominantly occurred when the NO3-N concentration was decreasing. In conclusion, the design and implementation of strategies for the control of NO3-N downstream should be carried out after performing a quantitative analysis of the impact of different control measures for different downstream conditions using a water quality model.
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The World Health Organization (WHO) and the U.S. Environmental Protection Agency (EPA) provide guidelines on the maximum levels of nitrate nitrogen (NO3-N) contained in drinking water since excess nitrate ingestion may harm human health. Thus, monitoring and controlling the NO3-N concentration is of paramount importance, especially in sources of drinking water such as the Nakdong River in South Korea. This study addresses NO3-N pollution in the Nakdong River in South Korea, where such pollution mostly comes from diffuse sources in the catchment due to the agricultural use of fertilizers. The objective of this study is to suggest guidelines for designing strategies to control NO3-N in this river using a process-based model developed with HEC-RAS. The model was built based on water quality parameters (water temperature, dissolved oxygen, ammonia nitrogen, etc.) related to NO3-N dynamics incorporating hydraulic and meteorological data. This model simulated NO3-N dynamics downstream under 55 scenarios while focusing on a section near locations of drinking water intakes. The scenarios were constructed based on variations in water quantity and quality upstream. The simulation results showed that the peak concentration of NO3-N downstream could be directly controlled by limiting the NO3-N concentration upstream. Additionally, control of the flow rate upstream could also lead to a reduction in the overall average concentration of NO3-N downstream, but this predominantly occurred when the NO3-N concentration was decreasing. In conclusion, the design and implementation of strategies for the control of NO3-N downstream should be carried out after performing a quantitative analysis of the impact of different control measures for different downstream conditions using a water quality model.
A Framework for Optimal Reservoir Operation to Improve Downstream Aquatic Environment
Application to Nakdong River Basin in South Korea
In the water sector, issues concerning the aquatic environment have been extensively discussed due to climate change. In particular, water quality problems such as harmful cyanobacterial blooms (CyanoHABs) in rivers have arisen in South Korea since 2012. The Korean government constructed 16 weirs in the rivers during the Four Major Rivers Restoration Project. These weirs were built to more effectively use water resources in the rivers. Many environmental activists, however, have claimed that the weirs have caused water quality problems of CyanoHABs in the rivers. These CyanoHABs can be threats to the water environment while harming human health and aquatic ecosystems since CyanoHABs produce toxic substances such as microcystins.
To address the problems of these CyanoHABs, many researchers have conducted studies on predictive models for CyanoHABs. A predictive model using a data-driven approach can be useful in exploring the main factors affecting CyanoHABs at a specific location. However, these studies have not focused on preventing the occurrence of CyanoHABs but only on predicting their occurrence. If these studies are designed to link with a practical method for reducing the frequency of CyanoHABs, viable strategies can be proposed to effectively control CyanoHABs. Therefore, detailed considerations are required concerning the prevention or mitigation of CyanoHABs.
Reservoir operation can be a solution for reducing the problem of CyanoHABs in a downstream river. For example, discharging more water from upstream reservoirs can flush CyanoHABs downstream. However, the risk of water shortage can be increased in a reservoir if it is operated for improving water quality downstream. This is because reservoirs were typically designed for management of water quantity such as water supply. To use limited water resources in a reservoir to reduce the frequency of CyanoHABs downstream, optimal reservoir operations are necessary that simultaneously consider both the quantity and the quality of water.
This study focused on establishing a practical framework for the optimal operation of upstream reservoirs to address the problem of CyanoHABs in a downstream river. Furthermore, the applicability of this framework was demonstrated using observational data related to the quantity and quality of the upstream reservoirs in the study area, the Nakdong River basin of South Korea. The framework was established by incorporating three models: a machine learning model, a river water quality model, and an optimization model for reservoir operation.
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In the water sector, issues concerning the aquatic environment have been extensively discussed due to climate change. In particular, water quality problems such as harmful cyanobacterial blooms (CyanoHABs) in rivers have arisen in South Korea since 2012. The Korean government constructed 16 weirs in the rivers during the Four Major Rivers Restoration Project. These weirs were built to more effectively use water resources in the rivers. Many environmental activists, however, have claimed that the weirs have caused water quality problems of CyanoHABs in the rivers. These CyanoHABs can be threats to the water environment while harming human health and aquatic ecosystems since CyanoHABs produce toxic substances such as microcystins.
To address the problems of these CyanoHABs, many researchers have conducted studies on predictive models for CyanoHABs. A predictive model using a data-driven approach can be useful in exploring the main factors affecting CyanoHABs at a specific location. However, these studies have not focused on preventing the occurrence of CyanoHABs but only on predicting their occurrence. If these studies are designed to link with a practical method for reducing the frequency of CyanoHABs, viable strategies can be proposed to effectively control CyanoHABs. Therefore, detailed considerations are required concerning the prevention or mitigation of CyanoHABs.
Reservoir operation can be a solution for reducing the problem of CyanoHABs in a downstream river. For example, discharging more water from upstream reservoirs can flush CyanoHABs downstream. However, the risk of water shortage can be increased in a reservoir if it is operated for improving water quality downstream. This is because reservoirs were typically designed for management of water quantity such as water supply. To use limited water resources in a reservoir to reduce the frequency of CyanoHABs downstream, optimal reservoir operations are necessary that simultaneously consider both the quantity and the quality of water.
This study focused on establishing a practical framework for the optimal operation of upstream reservoirs to address the problem of CyanoHABs in a downstream river. Furthermore, the applicability of this framework was demonstrated using observational data related to the quantity and quality of the upstream reservoirs in the study area, the Nakdong River basin of South Korea. The framework was established by incorporating three models: a machine learning model, a river water quality model, and an optimization model for reservoir operation.
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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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.
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.
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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.