• Korean Journal of Ecology and Environment
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• 제56권3호
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• pp.250-258
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• 2023

Freshwater jellyfish, a type of jellyfish exclusively found in freshwater, has a limited number of species but is found globally. However, their ecology and causes of occurrence are largely unknown. Therefore, understanding the distribution of polyps, which produce the larvae of freshwater jellyfish, can provide important data for comprehending their ecology. This study aims to explore the COI gene of freshwater jellyfish using environmental DNA from the microbial film in the Miho River system. Among the 12 survey points in the Miho River watershed, genetic material of freshwater jellyfish was detected in 8 points, mainly located upstream near reservoirs. These genetic materials were identified as genes of the well-known freshwater jellyfish species, Craspedacusta sowerbii. Notably, the C. sowerbii genes found in the Miho River watershed survey points were closely related to a species previously discovered in Italy. Consequently, utilizing environmental DNA to explore the genetic traces of freshwater jellyfish enables rapid screening of areas with a high likelihood of freshwater jellyfish occurrence. This approach is deemed to provide crucial information for understanding the distribution and ecology of freshwater jellyfish in Korea.

• Korean Journal of Ecology and Environment
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• 제56권3호
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Journal of the Institute of Convergence Signal Processing
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• 제24권1호
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• pp.27-33
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• 2023

Recently, water shortages have occurred due to climate change, and the need for water management of agricultural water has increased due to the occurrence of algal blooms in reservoirs. Existing algae prevention is operated by putting many people on site and misses the optimal spraying time due to movement through boats. In order to solve this problem, it is necessary to block contamination in advance and move within time to uniformly spray complex microorganisms uniformly. Control drones are used for pesticide spraying and can be applied to algae prevention work by utilizing control drones. In this paper, basic research for the establishment of a marine control system was conducted for application to the reservoir environment, and as one of the results, the characteristics of a drone nozzle, a core technology that can be used for control drones, were calculated. In particular, it was found that the existing agricultural control drones had a disadvantage that the concentration was non-uniform within the suggested spraying interval, and to compensate for this, nozzle positioning and nozzle spraying uniformity were calculated. Based on the experimental results, we develop a core algorithm for establishing an algal bloom monitoring system in the reservoir environment and propose a precision control technology that can be used for marine control work in the future.

• Economic and Environmental Geology
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• 제57권1호
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Journal of the Korean GEO-environmental Society
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• 제25권3호
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• pp.5-11
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• 2024

Promising geological structures for carbon dioxide capture and subsurface storage include aquifers, depleted reservoirs, and gas fields. Among these, aquifers are gaining attention due to their potential for storing significant amounts of carbon dioxide compared to other geological structures. Therefore, there is a growing interest in enhancing carbon dioxide storage efficiency by understanding the characteristics of aquifers and developing technologies tailored to their properties. In this study, the storage efficiency of carbon dioxide injection following surfactant pre-injection into porous micro-models was evaluated. The results indicate that as the concentration of the surfactant solution injected prior to carbon dioxide injection increases, storage efficiency improves. Conversely, lower concentrations require more surfactant injection to enhance storage efficiency. Furthermore, under identical surfactant concentration conditions, the storage efficiency from surfactant pre-injection prior to supercritical carbon dioxide injection is approximately 30% lower compared to surfactant-co-solvent substitution as observed in previous studies. However, under the maximum concentration conditions investigated in this study, similar storage efficiencies to those of previous studies were achieved. These findings are expected to guide concentration determinations for surfactant application aimed at enhancing carbon dioxide storage efficiency in aquifers in future studies.

• Journal of Korean Society of Disaster and Security
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• 제16권4호
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• pp.75-84
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• 2023

Many developing countries face challenges in estimating long-term discharge due to the lack of hydrological data for water supply planning, making it difficult to establish a rational water supply plan for decision-making on water distribution. The study area, the Bandung region in Indonesia, is experiencing rapid urbanization and population concentration, leading to a severe shortage of freshwater. The absence of water reservoir prediction methods has resulted in a water supply rate of approximately 20%. In this study, we aimed to propose an approach for predicting water reservoirs in developing countries by analyzing water safety and potential water supply using the MODSIM (Modified SIMYLD) network model. To assess the suitability of the MODSIM model, we applied the unit hydrograph method to calculate long-term discharge based on 19 years of discharge data (2002-2020) from the Pataruman observation station. The analysis confirmed alignment with the existing monthly optimal operation curve. The analysis of power plant capacity revealed a difference of approximately 0.30% to 0.50%, and the water intake safety at the Pataruman point showed 1.64% for Q95% flow and 0.47% for Q355 flow higher. Operational efficiency, compared to the existing reservoir optimal operation curve, was measured at around 1%, confirming the potential of using the MODSIM network model for water supply evaluation and the need for water supply facilities.

• Journal of the Korea Society of Computer and Information
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• 제29권3호
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Journal of the Korean GEO-environmental Society
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• 제25권6호
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• pp.5-12
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• 2024

Currently, a grouting method that minimizes damage to the reservoir embankment by injecting solidification agent at low pressure is commonly used to ensure waterproofing and safety of the embankment, but the use of solidification agents can cause issues, such as a decrease in durability and a lack of clear method for determining the mixing ratio. In this study, when the base ground and solidification agent were stirred and mixed at various weight mixing ratios, the permeability coefficient and strength of the mixture were confirmed through laboratory tests, and the optimal mixing ratio was suggested through analysis of the test results. The specimen for the laboratory test was produced considering the mixing ratio of the solidification agent. The specimen for the permeability coefficient test was tested by producing one each of cohesionless and cohesive soil for a mixing amount of 1.5 kN/m³ of solidification agent, and the permeability test results confirmed that the water barrier performance was secured below the permeability coefficient value required by various design criteria. A total of 24 specimens for the strength test were produced, 3 for each of 5 mixing amounts for cohesive soil and 3 mixing amounts for cohesionless soil. The strength test results showed that the uniaxial compressive strength tends to increase linearly with increasing curing time for both cohesionless soil and cohesive soil when the mixing amount is less than 2.0 kN/m³. Therefore, the optimal mixing ratio applied to the site is determined to be mixing amount of 1.5 kN/m³ and 2.0 kN/m³. Finally, numerical analysis reflecting test results was conducted on design case for improvement projects for aging reservoirs embankment to verify the water barrier performance and safety improvement effects.

• Journal of Marine Bioscience and Biotechnology
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• 제16권1호
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• pp.63-86
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• 2024

This study analyzed the distribution, diversity, and density variation of algal clusters in a freshwater reservoir from an oceanic island and a traditional inland water system to gain insights on future marine freshwater resource management. In the Paldang water system (Han River), despite the upstream Paldang Dam and the downstream Jamsil underwater reservoir being in the same meteorological zone, their algae density patterns varied inversely. The distinct algal cluster structure (diversity/dominance) of Paldang was altered in the downstream reservoir, suggesting that physical devices aid algae management in traditional water systems. In contrast, 24 out of 35 genera (63.2%) identified in the Jeolgol Reservoir (Baeknyeong Island) were unique, lacking regulatory mechanisms, and existing in a complex ecotone. The desmid Chlorophyceae Cosmarium, adapted to higher photosynthetic stress and low temperatures, dominated in January (38.04%) and August (86.45%) during the periods of extreme photosynthetic stress. Jeolgol's annual algal cluster structure (H' 2.097; D 0.259; S' 35) demonstrated higher stability than Paldang (H' 1.125; D 0.448; S' 13) and the Jamsil underwater reservoir (H' 1.078; D 0.469; S' 12), maintaining an H' above 1.5 even during midwinters. No evidence of TN/TP inflow from surrounding soils was observed, even during torrential rainfalls, with phosphorus being the limiting factor for algal growth. TOC, BOD, chlorophyll-a, and turbidity peaked during Cosmarium bloom. Future climate change is expected to cause fluctuations in algal clusters and related water quality factors. The complex transitional nature of the Jeolgol Reservoir, its algal diversity, and the interspecies interactions contribute to the high stability of its algal community.

• Journal of Korea Water Resources Association
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• 제57권2호
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.