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Yongeun Park
School of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea

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Journal article
Published: 12 June 2021 in Journal of Environmental Management
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Hydrodynamic and water quality modeling have provided valuable simulation results that have enhanced the understanding of the spatial and temporal distribution of algal blooms. Typical model simulations are performed with point-based observational data that are used to configure initial and boundary conditions, and for parameter calibration. However, the application of such conventional modeling approaches is limited due to cost, labor, and time constraints that preclude the retrieval of high-resolution spatial data. Thus, the present study applied fine-resolution algal data to configure the initial conditions of a hydrodynamic and water quality model and compared the accuracy of short-term algal simulations with the results simulated using conventional point-based initial conditions. The environmental fluid dynamics code (EFDC) model was calibrated to simulate Chlorophyll-a (Chl-a) concentrations. Hyperspectral images were used to generate Chl-a maps based on a two-band ratio algorithm for configuring the initial condition of the EFDC model. The model simulation with hyperspectral-based initial conditions returned relatively accurate results for Chl-a, compared to the simulation based on point-based initial conditions. The simulations exhibited percent bias values of 9.93 and 14.23, respectively. Therefore, the results of this study demonstrate how hyperspectral-based initial conditions could improve the reliability of short-term algal bloom simulations in a hydrodynamic model.

ACS Style

Jongcheol Pyo; Yong Sung Kwon; Joong-Hyuk Min; Gibeom Nam; Yong-Sik Song; Jung Min Ahn; Sanghyun Park; Jeongwon Lee; Kyung Hwa Cho; Yongeun Park. Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models. Journal of Environmental Management 2021, 294, 112988 .

AMA Style

Jongcheol Pyo, Yong Sung Kwon, Joong-Hyuk Min, Gibeom Nam, Yong-Sik Song, Jung Min Ahn, Sanghyun Park, Jeongwon Lee, Kyung Hwa Cho, Yongeun Park. Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models. Journal of Environmental Management. 2021; 294 ():112988.

Chicago/Turabian Style

Jongcheol Pyo; Yong Sung Kwon; Joong-Hyuk Min; Gibeom Nam; Yong-Sik Song; Jung Min Ahn; Sanghyun Park; Jeongwon Lee; Kyung Hwa Cho; Yongeun Park. 2021. "Effect of hyperspectral image-based initial conditions on improving short-term algal simulation of hydrodynamic and water quality models." Journal of Environmental Management 294, no. : 112988.

Research article
Published: 22 January 2021 in Journal of Sensors
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In the recent era of AI, instance segmentation has significantly advanced boundary and object detection especially in diverse fields (e.g., biological and environmental research). Despite its progress, edge detection amid adjacent objects (e.g., organism cells) still remains intractable. This is because homogeneous and heterogeneous objects are prone to being mingled in a single image. To cope with this challenge, we propose the weighted Mask R-CNN designed to effectively separate overlapped objects in virtue of extra weights to adjacent boundaries. For numerical study, a range of experiments are performed with applications to simulated data and real data (e.g., Microcystis, one of the most common algae genera and cell membrane images). It is noticeable that the weighted Mask R-CNN outperforms the standard Mask R-CNN, given that the analytic experiments show on average 92.5% of precision and 96.4% of recall in algae data and 94.5% of precision and 98.6% of recall in cell membrane data. Consequently, we found that a majority of sample boundaries in real and simulated data are precisely segmented in the midst of object mixtures.

ACS Style

Sungmin Suh; Yongeun Park; Kyoungmin Ko; Seongmin Yang; Jaehyeong Ahn; Jae-Ki Shin; Sunghwan Kim. Weighted Mask R-CNN for Improving Adjacent Boundary Segmentation. Journal of Sensors 2021, 2021, 1 -8.

AMA Style

Sungmin Suh, Yongeun Park, Kyoungmin Ko, Seongmin Yang, Jaehyeong Ahn, Jae-Ki Shin, Sunghwan Kim. Weighted Mask R-CNN for Improving Adjacent Boundary Segmentation. Journal of Sensors. 2021; 2021 ():1-8.

Chicago/Turabian Style

Sungmin Suh; Yongeun Park; Kyoungmin Ko; Seongmin Yang; Jaehyeong Ahn; Jae-Ki Shin; Sunghwan Kim. 2021. "Weighted Mask R-CNN for Improving Adjacent Boundary Segmentation." Journal of Sensors 2021, no. : 1-8.

Journal article
Published: 27 January 2020 in Journal of Environmental Management
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Green roof can mitigate urban stormwater and improve environmental, economic, and social conditions. Various modeling approaches have been effectively employed to implement a green roof, but previous models employed simplifications to simulate water movement in green roof systems. To address this issue, we developed a new modeling tool (SWMM-H) by coupling the stormwater management and HYDRUS-1D models to improve simulations of hydrological processes. We selected green roof systems to evaluate the coupled model. Rainfall-runoff experiments were conducted for a pilot-scale green roof and urban subbasin. Soil moisture in the green roof and runoff volume in the subbasin were simulated more accurately by using SWMM-H instead of SWMM. The scenario analysis showed that SWMM-H selected sandy loam for controlling runoff whereas SWMM recommended sand. In conclusion, SWMM-H could be a useful tool for accurately understanding hydrological processes in green roofs.

ACS Style

Sangsoo Baek; Mayzonee Ligaray; Yakov Pachepsky; Jong Ahn Chun; Kwang-Sik Yoon; Yongeun Park; Kyung Hwa Cho. Assessment of a green roof practice using the coupled SWMM and HYDRUS models. Journal of Environmental Management 2020, 261, 109920 .

AMA Style

Sangsoo Baek, Mayzonee Ligaray, Yakov Pachepsky, Jong Ahn Chun, Kwang-Sik Yoon, Yongeun Park, Kyung Hwa Cho. Assessment of a green roof practice using the coupled SWMM and HYDRUS models. Journal of Environmental Management. 2020; 261 ():109920.

Chicago/Turabian Style

Sangsoo Baek; Mayzonee Ligaray; Yakov Pachepsky; Jong Ahn Chun; Kwang-Sik Yoon; Yongeun Park; Kyung Hwa Cho. 2020. "Assessment of a green roof practice using the coupled SWMM and HYDRUS models." Journal of Environmental Management 261, no. : 109920.

Journal article
Published: 17 November 2019 in Energies
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This study systematically investigated the feasibility of the microbubble ozonation process to degrade the 17α-ethinylestradiol, ibuprofen, and atenolol through the comparison with the millibubble ozonation process for elucidating the degradation behavior and mechanisms during the microbubble ozonation processes. The proportions of small microbubbles (diameter 1–25 μm) were increased with increasing the cavity pump frequency (40 Hz: 51.4%; 50 Hz: 57.5%; 60 Hz: 59.9%). The increased concentrations of O3 and OH radicals due to the higher specific area of O3 microbubbles compared to O3 millibubbles could facilitate their mass transfer at the gas–water interface. Furthermore, the elevated reactivity of O3 by increasing the temperature might improve the degradation of the pharmaceutical compounds, which was more pronounced for the microbubble ozonated waters than the millibubble ozonated waters. Although the degradation efficiency of the pharmaceutical compounds during the microbubble ozonation processes was significantly influenced by the existence of humic acids compared to the millibubble ozonation process, the increased solubilization rate of O3 and OH radicals by collapsing O3 microbubbles enhanced the degradation of the pharmaceutical compounds. Overall, these results clearly showed that the microbubble ozonation process could be an alternative option to conventional ozonation processes for the abatement of the pharmaceutical compounds.

ACS Style

Yong-Gu Lee; Yongeun Park; Gwanghee Lee; Yeongkwan Kim; Kangmin Chon. Enhanced Degradation of Pharmaceutical Compounds by a Microbubble Ozonation Process: Effects of Temperature, pH, and Humic Acids. Energies 2019, 12, 4373 .

AMA Style

Yong-Gu Lee, Yongeun Park, Gwanghee Lee, Yeongkwan Kim, Kangmin Chon. Enhanced Degradation of Pharmaceutical Compounds by a Microbubble Ozonation Process: Effects of Temperature, pH, and Humic Acids. Energies. 2019; 12 (22):4373.

Chicago/Turabian Style

Yong-Gu Lee; Yongeun Park; Gwanghee Lee; Yeongkwan Kim; Kangmin Chon. 2019. "Enhanced Degradation of Pharmaceutical Compounds by a Microbubble Ozonation Process: Effects of Temperature, pH, and Humic Acids." Energies 12, no. 22: 4373.

Journal article
Published: 16 November 2019 in Remote Sensing of Environment
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The remote sensing of algal pigments is essential for understanding the temporal and spatial distribution of harmful algal blooms (HABs). In particular, the vertical distribution of cyanobacterial pigment (e.g., phycocyanin (PC)) is critical factor in remote sensing because the diel vertical migration of cyanobacteria may affect the spectral signals according to observational time. Although numerous studies have been conducted on the remote sensing of algal bloom using pigments, few studies considered the vertical distribution of the pigments for the remote sensing of cyanobacteria in inland waters. In this regard, the objective of this study was to develop an improved bio-optical remote-sensing method using in-situ remote-sensing reflectance (Rrs) at different water depths and cumulative PC and Chlorophyll-a (Chl-a) concentrations, which was cumulated from the surface to a 5-m water depth. The results showed that the bio-optical algorithm using surface Rrs and surface pigment concentration was more accurate than that using the subsurface Rrs and surface pigments. The bio-optical algorithm using subsurface Rrs showed the highest R-squared (R2) values (0.87–0.94) in each regression with the cumulative PC concentration from surface to each depth. The regressions between drone-based surface reflectance and cumulative PC concentration for each depth indicated a better performance than those between the reflectance and surface PC concentration; the highest R2 value of 0.82 was obtained from a bio-optical algorithm using drone-based reflectance and a 1.0-m cumulative PC concentration, which was the best-performing algorithm. The PC maps developed using the best bio-optical algorithm accurately described the spatial and temporal distributions of the PC concentrations in the reservoir. This study demonstrates that the application of vertical cumulative pigment concentration and subsurface Rrs measurement in bio-optical algorithms can improve their performance in estimating pigments, and that drone-based hyperspectral imagery is an efficient tool for the remote sensing of cyanobacterial pigments over a wide area.

ACS Style

Yong Sung Kwon; Jongcheol Pyo; Hongtao Duan; Kyung Hwa Cho; Yongeun Park. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sensing of Environment 2019, 236, 111517 .

AMA Style

Yong Sung Kwon, Jongcheol Pyo, Hongtao Duan, Kyung Hwa Cho, Yongeun Park. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sensing of Environment. 2019; 236 ():111517.

Chicago/Turabian Style

Yong Sung Kwon; Jongcheol Pyo; Hongtao Duan; Kyung Hwa Cho; Yongeun Park. 2019. "Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir." Remote Sensing of Environment 236, no. : 111517.

Proceedings article
Published: 11 October 2018 in Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018
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Frequency and intensity of the harmful algal blooms (HABs) increased globally since 1970s. The increase in HABs have negatively affected aquatic ecosystem and aquaculture industry. The economic losses were about $ 1 billion in Europe, $ 100 million in USA and $ 121 billion in Korea per year. There were various field monitoring campaigns for ecological and biological researches. However, traditional HABs monitoring has limitations on both spatial and temporal coverage. In these days, multispectral remote sensing methods using satellite sensors have been widely used to monitor HABs in ocean and coastal areas. However, the satellite systems used in ocean and coastal research, such as MODIS, SeaWiFS and etc. have limitations in study on complex coastline, because of their coarse spatial resolution (~ few km). In this research, we conducted two-year intensive monitoring on the South Sea of Korea from 2016 to 2017 at 62 sampling station and used landsat-8 operational land imager (OLI) satellite that has 30m spatial resolution. We used 4 band (band 1 to 4), 4-band ratio (band 1 over band 3 and 4, and band 2 over band 3 and 4) and mixed dataset of 4 band and 4-band ratio. The empirical OC algorithms showed poor performances, under 0.25 of r-squared. The machine learning techniques, i.e., artificial neural network (ANN) and support vector machine (SVM) were applied to enhance performance of estimating chl-a on landsat-8 application. Parameters for developing ANN and SVM model were optimized using a pattern search algorithm in MATLAB toolbox. All dataset were divided into 80 % of training and 20 % of validation data. In the training step, mixed dataset showed the best performance in both ANN and SVM models, whereas 4-band ratio and 4 band dataset in the validation step showed the best performance in ANN and SVM, respectively. The ANN model showed poor performance in low chl-a concentrations but SVM had more accurate performance in low and mid concentrations. Both models under-estimated chl-a in mid to high concentration range. For the mapping results, the ANN model using 4 band dataset showed very low concentration of chl-a in most of research area, whereas SVM showed high concentration of chl-a in coastal area and bay. The result using 4-band ratio dataset showed similar chl-a distribution in ANN and SVM. For mixed dataset results the ANN model estimated over 8 mg m-3 of chl-a at some of coastal, almost zero in near coastal area and over 2 mg m-3 chl-a concentration for off-shore area. In case of SVM, all region showed approximately 2 mg m-3 of chl-a concentration. Landsat-8 OLI was not proper system for OC algorithms. Machine learning techniques were effective tools for enhancing ocean chl-a estimation performance using landsat-8 OLI. Thus, this study showed potential of landsat-8 OLI application to coastal HAB monitoring.

ACS Style

Yongsung Kwon; Seung Ho Baek; Young Kyun Lim; Jongcheol Pyo; Yongeun Park; Kyung Hwa Cho. Coastal chlorophyll-a concentrations monitoring in complex coastal region using machine learning techniques (Conference Presentation). Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018 2018, 10784, 1078403 .

AMA Style

Yongsung Kwon, Seung Ho Baek, Young Kyun Lim, Jongcheol Pyo, Yongeun Park, Kyung Hwa Cho. Coastal chlorophyll-a concentrations monitoring in complex coastal region using machine learning techniques (Conference Presentation). Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018. 2018; 10784 ():1078403.

Chicago/Turabian Style

Yongsung Kwon; Seung Ho Baek; Young Kyun Lim; Jongcheol Pyo; Yongeun Park; Kyung Hwa Cho. 2018. "Coastal chlorophyll-a concentrations monitoring in complex coastal region using machine learning techniques (Conference Presentation)." Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018 10784, no. : 1078403.

Proceedings article
Published: 11 October 2018 in Earth Resources and Environmental Remote Sensing/GIS Applications IX
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Remote sensing is useful technique to not only detect harmful algal bloom but also quantify the concentration of the harmful algae by using surface reflectance data. The hyperspectral image allows to investigate detail spatial information of harmful algae. Atmospheric correction is critical process in order to achieve an accurate optical signal in water surface. However, the influence of atmospheric correction performance on the estimation of PC concentration has rarely been studied. Thus, this study investigated the influence of three atmospheric correction method on the spatial distribution and concentration of phycocyanin (PC) concentration from estimation of the bio-optical algorithms. Atmospheric correction is an important image processing technique of hyperspectral image because the result of the correction is expected to influence bio-optical algorithms for quantifying phycocyanin (PC) concentration. From August to October, four field and airborne monitoring campaigns in the Baekje Weir in South Korea were implemented to measure water surface reflectance. PC concentrations in the surface water were analyzed using freezing and thawing method. The two band ratio algorithm, the three band ratio algorithm, the Li algorithm, and the Simis algorithm were utilized to estimate PC concentration. And, atmospheric correction of hyperspectral image was conducted by MODTRAN 6, ATCOR 4, and an artificial neural network (ANN). The ANN model utilized atmospheric parameters which generated from MODTRAN 6 to simulate water surface reflectance. Bio-optical algorithms were applied to the atmospherically corrected image for generating PC distribution map. Even though the atmospheric correction result from the ANN showed Nash-Sutcliffe efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively, the PC concentration from the bio-optical algorithms showed NSE values ranged from 0.17 to 0.57. The ANN model was turned out to be required having large quantities of input data in order to have precise simulation performances. Atmospheric correction from MODTRAN 6 showed an NSE value over 0.8, whereas the correction from ATCOR 4 had a negative NSE value. However, the accuracy in certain regions of the reflectance spectra (λ700 nm) was relatively low compared to the other spectra region because of insufficient atmospheric observation data during monitoring period. The relationship between atmospheric correction and bio-optical algorithm performance for MODTRAN 6, ATCOR 4, and ANN simulation showed different estimation of PC concentration in the atmospherically corrected images. The precise atmospheric correction by MODTRAN 6 enabled an accurate bio-optical algorithm performance and thus was critical to generating a satisfactory spatial distribution map of PC. In addition, the Li and Simis algorithms were revealed to be much more sensitive to the performance of the atmospheric correction than the other algorithms. Therefore, this...

ACS Style

Jongcheol Pyo; Yong Sung Kwon; Yongeun Park; Kyung Hwa Cho. Effect of atmospheric correction performance on quantify cyanobacteria concentration using hyperspectral imager sensing (Conference Presentation). Earth Resources and Environmental Remote Sensing/GIS Applications IX 2018, 10790, 1079004 .

AMA Style

Jongcheol Pyo, Yong Sung Kwon, Yongeun Park, Kyung Hwa Cho. Effect of atmospheric correction performance on quantify cyanobacteria concentration using hyperspectral imager sensing (Conference Presentation). Earth Resources and Environmental Remote Sensing/GIS Applications IX. 2018; 10790 ():1079004.

Chicago/Turabian Style

Jongcheol Pyo; Yong Sung Kwon; Yongeun Park; Kyung Hwa Cho. 2018. "Effect of atmospheric correction performance on quantify cyanobacteria concentration using hyperspectral imager sensing (Conference Presentation)." Earth Resources and Environmental Remote Sensing/GIS Applications IX 10790, no. : 1079004.

Journal article
Published: 01 October 2018 in Water Environment Research
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The review includes scientific literatures published in the year of 2017 regarding the uses of biofilm and bioreactor to treat wastewater. Topics considered are: biofilm formation and factors that impact biofilm formation; extracellular polymeric substance from biofilms; biofilm consortia and quorum sensing; biofilm associated phage; biofilm reactors; and biofilm in bioelectrochemical systems.

ACS Style

Eun-Sik Kim; Youngjo Kim; Yongeun Park; Jeongdong Choi. Biological Fixed Film. Water Environment Research 2018, 90, 900 -927.

AMA Style

Eun-Sik Kim, Youngjo Kim, Yongeun Park, Jeongdong Choi. Biological Fixed Film. Water Environment Research. 2018; 90 (10):900-927.

Chicago/Turabian Style

Eun-Sik Kim; Youngjo Kim; Yongeun Park; Jeongdong Choi. 2018. "Biological Fixed Film." Water Environment Research 90, no. 10: 900-927.

Journal article
Published: 01 September 2018 in Journal of Environmental Quality
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Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and Escherichia coli) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations (p < 0.01), whereas solar radiation was negatively correlated (p < 0.01). The performance of the ANN model for predicting ENT and E. coli at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset (p < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use. Copyright © 2018. . Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

ACS Style

Yongeun Park; Minjeong Kim; Yakov Pachepsky; Seoung-Hwa Choi; Jeong-Goo Cho; Junho Jeon; Kyung Hwa Cho. Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea. Journal of Environmental Quality 2018, 47, 1094 -1102.

AMA Style

Yongeun Park, Minjeong Kim, Yakov Pachepsky, Seoung-Hwa Choi, Jeong-Goo Cho, Junho Jeon, Kyung Hwa Cho. Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea. Journal of Environmental Quality. 2018; 47 (5):1094-1102.

Chicago/Turabian Style

Yongeun Park; Minjeong Kim; Yakov Pachepsky; Seoung-Hwa Choi; Jeong-Goo Cho; Junho Jeon; Kyung Hwa Cho. 2018. "Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea." Journal of Environmental Quality 47, no. 5: 1094-1102.

Journal article
Published: 02 August 2018 in Water
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Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.

ACS Style

Yong Sung Kown; Seung Ho Baek; Young Kyun Lim; Jongcheol Pyo; Mayzonee Ligaray; Yongeun Park; Kyung Hwa Cho; Yong Sung Kwon. Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models. Water 2018, 10, 1020 .

AMA Style

Yong Sung Kown, Seung Ho Baek, Young Kyun Lim, Jongcheol Pyo, Mayzonee Ligaray, Yongeun Park, Kyung Hwa Cho, Yong Sung Kwon. Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models. Water. 2018; 10 (8):1020.

Chicago/Turabian Style

Yong Sung Kown; Seung Ho Baek; Young Kyun Lim; Jongcheol Pyo; Mayzonee Ligaray; Yongeun Park; Kyung Hwa Cho; Yong Sung Kwon. 2018. "Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models." Water 10, no. 8: 1020.

Journal article
Published: 01 August 2018 in Ecological Engineering
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Climate change is a primary driver that alters water pollution hotspots in watersheds, which complicates the use of Best management practices (BMPs) for diffuse pollution mitigation strategies. The objective of this study is to evaluate future changes in BMPs on total phosphorus (TP) loads to the river system, depending on climate change. Current weather data were collected from 2000 to 2010 and future weather data from 2040 to 2050 were obtained from regional climate model simulations based on Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 scenarios. This study describes various BMP implementation plans at affordable cost that are adapted to different weather conditions, current (for 2000–2010) and future climate scenarios (for 2040–2050). The Soil and Water Assessment Tool (SWAT), i.e., one of the deterministic models for watershed management, was used to estimate total phosphorus (TP) removal efficiency of various types of BMP at two crop fields (rice paddy and soybean fields) in the Yeongsan River (YR) watershed of Korea. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the artificial intelligence (AI) models for decision support framework, was applied to obtain allow for trade-off analysis between two conflict objectives, the maximum TP load reduction against implementation costs. The SWAT simulation results showed that the model performance based on the Nash-Sutcliffe efficiency and percent bias was acceptable for simulating three parameters; daily flow discharge, monthly sediment loads, and TP loads in the river. As a result, monthly sediment loads and TP loads increase remarkably under all future climate change scenarios except for July, when a comparable amount of precipitation is recorded during the present and future conditions. New implementation of BMPs, therefore, will be required for future climate change scenarios to achieve 50% TP load reduction in the given watershed condition. Interestingly, parallel terraces which showed good efficiency in reducing TP loads at both fields under the current weather condition cannot be effective any more under the worst-case weather scenario in the future climate. In such a case, detention ponds can be proposed as BMP alternative to parallel terraces. Overall, this study not only demonstrates that watershed management plans using BMPs should be adjusted according to climate change scenarios, but also that the hybrid use of SWAT model and AI can help in refining existing and future BMPs at affordable cost.

ACS Style

Dong Jin Jeon; Seo Jin Ki; Yoonkyung Cha; Yongeun Park; Joon Ha Kim. New methodology of evaluation of best management practices performances for an agricultural watershed according to the climate change scenarios: A hybrid use of deterministic and decision support models. Ecological Engineering 2018, 119, 73 -83.

AMA Style

Dong Jin Jeon, Seo Jin Ki, Yoonkyung Cha, Yongeun Park, Joon Ha Kim. New methodology of evaluation of best management practices performances for an agricultural watershed according to the climate change scenarios: A hybrid use of deterministic and decision support models. Ecological Engineering. 2018; 119 ():73-83.

Chicago/Turabian Style

Dong Jin Jeon; Seo Jin Ki; Yoonkyung Cha; Yongeun Park; Joon Ha Kim. 2018. "New methodology of evaluation of best management practices performances for an agricultural watershed according to the climate change scenarios: A hybrid use of deterministic and decision support models." Ecological Engineering 119, no. : 73-83.

Journal article
Published: 26 July 2018 in Remote Sensing
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Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.

ACS Style

Jong Cheol Pyo; Mayzonee Ligaray; Yong Sung Kwon; Myoung-Hwan Ahn; Kyunghyun Kim; Hyuk Lee; Taegu Kang; Seong Been Cho; Yongeun Park; Kyung Hwa Cho. High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery. Remote Sensing 2018, 10, 1180 .

AMA Style

Jong Cheol Pyo, Mayzonee Ligaray, Yong Sung Kwon, Myoung-Hwan Ahn, Kyunghyun Kim, Hyuk Lee, Taegu Kang, Seong Been Cho, Yongeun Park, Kyung Hwa Cho. High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery. Remote Sensing. 2018; 10 (8):1180.

Chicago/Turabian Style

Jong Cheol Pyo; Mayzonee Ligaray; Yong Sung Kwon; Myoung-Hwan Ahn; Kyunghyun Kim; Hyuk Lee; Taegu Kang; Seong Been Cho; Yongeun Park; Kyung Hwa Cho. 2018. "High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery." Remote Sensing 10, no. 8: 1180.

Journal article
Published: 01 February 2018 in Science of The Total Environment
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The Agricultural Policy/Environmental eXtender (APEX) is a watershed-scale water quality model that includes detailed representation of agricultural management. The objective of this work was to develop a process-based model for simulating the fate and transport of manure-borne bacteria on land and in streams with the APEX model. The bacteria model utilizes manure erosion rates to estimate the amount of edge-of-field bacteria export. Bacteria survival in manure is simulated as a two-stage process separately for each manure application event. In-stream microbial fate and transport processes include bacteria release from streambeds due to sediment resuspension during high flow events, active release from the streambed sediment during low flow periods, bacteria settling with sediment, and survival. Default parameter values were selected from published databases and evaluated based on field observations. The APEX model with the newly developed microbial fate and transport module was applied to simulate fate and transport of the fecal indicator bacterium Escherichia coli in the Toenepi watershed, New Zealand that was monitored for seven years. The stream network of the watershed ran through grazing lands with daily bovine waste deposition. Results show that the APEX with the bacteria module reproduced well the monitored pattern of E. coli concentrations at the watershed outlet. The APEX with the microbial fate and transport module will be utilized for predicting microbial quality of water as affected by various agricultural practices, evaluating monitoring protocols, and supporting the selection of management practices based on regulations that rely on fecal indicator bacteria concentrations.

ACS Style

Eun-Mi Hong; Yongeun Park; Richard Muirhead; Jaehak Jeong; Yakov A. Pachepsky. Development and evaluation of the bacterial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model. Science of The Total Environment 2018, 615, 47 -58.

AMA Style

Eun-Mi Hong, Yongeun Park, Richard Muirhead, Jaehak Jeong, Yakov A. Pachepsky. Development and evaluation of the bacterial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model. Science of The Total Environment. 2018; 615 ():47-58.

Chicago/Turabian Style

Eun-Mi Hong; Yongeun Park; Richard Muirhead; Jaehak Jeong; Yakov A. Pachepsky. 2018. "Development and evaluation of the bacterial fate and transport module for the Agricultural Policy/Environmental eXtender (APEX) model." Science of The Total Environment 615, no. : 47-58.

Journal article
Published: 01 January 2018 in Desalination and Water Treatment
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Jongkwan Park; Chan Ho Lee; Kyung Hwa Cho; Seongho Hong; Young Mo Kim; Yongeun Park. Modeling trihalomethanes concentrations in water treatment plants using machine learning techniques. Desalination and Water Treatment 2018, 111, 125 -133.

AMA Style

Jongkwan Park, Chan Ho Lee, Kyung Hwa Cho, Seongho Hong, Young Mo Kim, Yongeun Park. Modeling trihalomethanes concentrations in water treatment plants using machine learning techniques. Desalination and Water Treatment. 2018; 111 ():125-133.

Chicago/Turabian Style

Jongkwan Park; Chan Ho Lee; Kyung Hwa Cho; Seongho Hong; Young Mo Kim; Yongeun Park. 2018. "Modeling trihalomethanes concentrations in water treatment plants using machine learning techniques." Desalination and Water Treatment 111, no. : 125-133.

Journal article
Published: 01 December 2017 in Water Research
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Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = -0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = -0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.

ACS Style

Yongeun Park; Jongcheol Pyo; Yong Sung Kwon; Yoonkyung Cha; Hyuk Lee; Taegu Kang; Kyung Hwa Cho. Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea. Water Research 2017, 126, 319 -328.

AMA Style

Yongeun Park, Jongcheol Pyo, Yong Sung Kwon, Yoonkyung Cha, Hyuk Lee, Taegu Kang, Kyung Hwa Cho. Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea. Water Research. 2017; 126 ():319-328.

Chicago/Turabian Style

Yongeun Park; Jongcheol Pyo; Yong Sung Kwon; Yoonkyung Cha; Hyuk Lee; Taegu Kang; Kyung Hwa Cho. 2017. "Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea." Water Research 126, no. : 319-328.

Journal article
Published: 22 November 2017 in Environmental Modelling & Software
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The ability to simulate algal systems is critical for watershed-scale models. The objective of this study was to develop and evaluate a modified algal module that simulates the dynamics of three major algal groups (cyanobacteria, green algae, and diatoms) in a stream using variables available in the Soil and Water Assessment Tool. The proposed module 1) models the dynamics of the three algal groups while accounting for nutrients from algal die-off and 2) has temperature multipliers that consider the effect of temperature changes on kinetic rates. Data to test the module were collected from a forest-dominated watershed over two years. The modified module was efficient in predicting seasonal variations in algal group biomass and simulated the regeneration of nutrients after algal die-off. This module will be useful in predicting the dynamics of the three studied algal groups and evaluating the best management practices for algal blooms in watersheds.

ACS Style

Jongcheol Pyo; Yakov A. Pachepsky; Minjeong Kim; Sang-Soo Baek; Hyuk Lee; Yoonkyung Cha; Yongeun Park; Kyung Hwa Cho. Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model. Environmental Modelling & Software 2017, 122, 104073 .

AMA Style

Jongcheol Pyo, Yakov A. Pachepsky, Minjeong Kim, Sang-Soo Baek, Hyuk Lee, Yoonkyung Cha, Yongeun Park, Kyung Hwa Cho. Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model. Environmental Modelling & Software. 2017; 122 ():104073.

Chicago/Turabian Style

Jongcheol Pyo; Yakov A. Pachepsky; Minjeong Kim; Sang-Soo Baek; Hyuk Lee; Yoonkyung Cha; Yongeun Park; Kyung Hwa Cho. 2017. "Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model." Environmental Modelling & Software 122, no. : 104073.

Articles
Published: 22 September 2017 in International Journal of Remote Sensing
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Harmful algal blooms have caused critical problems worldwide because they pose serious threats to human health and aquatic ecosystems. In particular, red tide blooms of Cochlodinium polykrikoides have caused serious damage to aquaculture in Korean coastal waters. In this study, multiple linear regression, regression tree (RT), and Random Forest models were applied to detect C. polykrikoides blooms in coastal waters. Five types of input data sets were implemented to test the performance of the models. The observed number of C. polykrikoides cells and reflectance data from Geostationary Ocean Color Imager images obtained in a 3-year period (2013–2015) were used to train and validate the models. The RT model demonstrated the best prediction performance when four bands and three-band ratio data were simultaneously used as input data. The results obtained via iterative model development with randomly chosen input data indicate that the recognition of patterns in the training data caused variations in the prediction performance. This work provides useful tools for reliable estimation of the number of C. polykrikoides cells using reasonable coastal water reflectance data sets. It is expected that administrators and decision-makers whose work is associated with coastal waters will be able to easily access and manipulate the RT model.

ACS Style

Yong Sung Kwon; Eunna Jang; Jungho Im; Seung Ho Baek; Yongeun Park; Kyung Hwa Cho. Developing data-driven models for quantifying Cochlodinium polykrikoides using the Geostationary Ocean Color Imager (GOCI). International Journal of Remote Sensing 2017, 39, 68 -83.

AMA Style

Yong Sung Kwon, Eunna Jang, Jungho Im, Seung Ho Baek, Yongeun Park, Kyung Hwa Cho. Developing data-driven models for quantifying Cochlodinium polykrikoides using the Geostationary Ocean Color Imager (GOCI). International Journal of Remote Sensing. 2017; 39 (1):68-83.

Chicago/Turabian Style

Yong Sung Kwon; Eunna Jang; Jungho Im; Seung Ho Baek; Yongeun Park; Kyung Hwa Cho. 2017. "Developing data-driven models for quantifying Cochlodinium polykrikoides using the Geostationary Ocean Color Imager (GOCI)." International Journal of Remote Sensing 39, no. 1: 68-83.

Journal article
Published: 22 June 2017 in Water
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Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds.

ACS Style

Mayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Jin-Sung Ra; Jong Ahn Chun; Yongeun Park; Laurie Boithias; Olivier Ribolzi; Kangmin Chon; Kyung Hwa Cho. Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines. Water 2017, 9, 451 .

AMA Style

Mayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Jin-Sung Ra, Jong Ahn Chun, Yongeun Park, Laurie Boithias, Olivier Ribolzi, Kangmin Chon, Kyung Hwa Cho. Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines. Water. 2017; 9 (7):451.

Chicago/Turabian Style

Mayzonee Ligaray; Minjeong Kim; Sangsoo Baek; Jin-Sung Ra; Jong Ahn Chun; Yongeun Park; Laurie Boithias; Olivier Ribolzi; Kangmin Chon; Kyung Hwa Cho. 2017. "Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines." Water 9, no. 7: 451.

Journal article
Published: 30 May 2017 in Remote Sensing
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Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned.

ACS Style

Jongcheol Pyo; Yakov Pachepsky; Sang-Soo Baek; Yongseong Kwon; Minjeong Kim; Hyuk Lee; Sanghyun Park; Yoonkyung Cha; Rim Ha; Gibeom Nam; Yongeun Park; Kyung Hwa Cho. Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea. Remote Sensing 2017, 9, 542 .

AMA Style

Jongcheol Pyo, Yakov Pachepsky, Sang-Soo Baek, Yongseong Kwon, Minjeong Kim, Hyuk Lee, Sanghyun Park, Yoonkyung Cha, Rim Ha, Gibeom Nam, Yongeun Park, Kyung Hwa Cho. Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea. Remote Sensing. 2017; 9 (6):542.

Chicago/Turabian Style

Jongcheol Pyo; Yakov Pachepsky; Sang-Soo Baek; Yongseong Kwon; Minjeong Kim; Hyuk Lee; Sanghyun Park; Yoonkyung Cha; Rim Ha; Gibeom Nam; Yongeun Park; Kyung Hwa Cho. 2017. "Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea." Remote Sensing 9, no. 6: 542.

Journal article
Published: 01 January 2017 in Journal of Environmental Quality
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Yongeun Park; Yakov Pachepsky; Eun-Mi Hong; Daniel Shelton; Cary Coppock. Escherichia coli Release from Streambed to Water Column during Baseflow Periods: A Modeling Study. Journal of Environmental Quality 2017, 46, 219 -226.

AMA Style

Yongeun Park, Yakov Pachepsky, Eun-Mi Hong, Daniel Shelton, Cary Coppock. Escherichia coli Release from Streambed to Water Column during Baseflow Periods: A Modeling Study. Journal of Environmental Quality. 2017; 46 (1):219-226.

Chicago/Turabian Style

Yongeun Park; Yakov Pachepsky; Eun-Mi Hong; Daniel Shelton; Cary Coppock. 2017. "Escherichia coli Release from Streambed to Water Column during Baseflow Periods: A Modeling Study." Journal of Environmental Quality 46, no. 1: 219-226.