Spatiotemporal and Longitudinal Variability of Hydro-meteorology, Basic Water Quality and Dominant Algal Assemblages in the Eight Weir Pools of Regulated River (Nakdong)

Shin, Jae-Ki; Park, Yongeun

doi:10.11614/ksl.2018.51.4.268

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…In South Korea, a large-scale national project was implemented to dredge rivers and install eco-friendly weirs to increase the water storage capacity and restore the ecosystems of the country’s four major rivers. However, since 2012, the flow velocity of rivers between weirs has decreased, leading to an increase in the frequency and intensity of algal blooms with HABs and risk of drinking water pollution [ 16 , 17 ]. In South Korea, an algal alert warning system is currently in place at 29 stations along four major rivers and reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

Kim,

Lee,

Byeon

et al. 2023

Toxics

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Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique–Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

Kim,

Lee,

Byeon

et al. 2023

Toxics

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“…investigated the phytoplankton community in the middle and lower reaches of the river from January to December 2012 and estimated the indices of species diversity and dominance. From January 2013 to July 2017 [10], conducted weekly examinations of the major environmental contributors to the water quality in eight of the river's weir pools and compared and analyzed their interrelationships with the dominant algal species [11]. used data on the chlorophyll a (Chl-a) content, algal species populations, water quality, and hydrological and meteorological factors in the Mulgeum area (a site located in the lower reaches of the Nakdong River) to correlate the changes in algal bloom patterns with changes in the influencing factors after construction of the weirs [12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data

Jo,

Jung,

Nam

et al. 2023

Applied Sciences

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A multifunctional weir was built on the Nakdong River. As a result, changes in the river environment occurred, such as an increase in river residence time. This causes changes in water quality, including green algae. The occurrence of green algae in the Nakdong River, which is used as a water source, also affects the purified water supply system. In particular, the mass spread of harmful algae is becoming a major problem as the frequency and intensity of occurrences increase. There are various causes of blue-green algae. We would like to examine the relationships between causal factors through a decision tree-based algorithm. Additionally, we would like to predict the occurrence of green algae based on the combination of these factors. For prediction, we studied categorical prediction based on the blue-green algae warning system used in Korea. RF, Catboost and XGBoost algorithms were used. Optimal hyperparameters were applied. We compared the prediction performance of each algorithm. In addition, the predictability of using sensor-based data was reviewed for a preemptive response to the occurrence of blue-green algae. By applying sensor-based data, the accuracy was over 80%. Prediction accuracy by category was also over 75%. It is believed that real-time prediction is possible through sensor-based factors. The optimal forecast period was analyzed to determine whether a preemptive response was possible and the possibility of improvement was examined through the segmentation of prediction categories. When there were three categories, 79% of predictions were possible by the 21st day. In seven categories, 75% prediction was possible up to 14 days. In this study, sensor-based categorical predictability was derived. In addition, real-time response and proactive response were determined. Such sensor-based algae prediction research is considered important for future blue-green algae management and river management.

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Allelopathic Inhibition Effects of Myriophyllum spicatum on Growths of Bloom-Forming Cyanobacteria and Other Phytoplankton Species in Coexistence Experiments

2021

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Spatiotemporal and Longitudinal Variability of Hydro-meteorology, Basic Water Quality and Dominant Algal Assemblages in the Eight Weir Pools of Regulated River (Nakdong)

Cited by 8 publications

References 48 publications

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

Machine Learning-Based Early Warning Level Prediction for Cyanobacterial Blooms Using Environmental Variable Selection and Data Resampling

Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data

Allelopathic Inhibition Effects of Myriophyllum spicatum on Growths of Bloom-Forming Cyanobacteria and Other Phytoplankton Species in Coexistence Experiments

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