
<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ronnel S Melad</style></author><author><style face="normal" font="default" size="100%">Raphael Luis V Nonato</style></author><author><style face="normal" font="default" size="100%">Dale Joseph Salazar</style></author><author><style face="normal" font="default" size="100%">Mayzonee V Ligaray</style></author><author><style face="normal" font="default" size="100%">Angelo Earvin Sy Choi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial Assessment of Water Quality in Mananga River in Talisay City, Cebu, Philippines</style></title><secondary-title><style face="normal" font="default" size="100%">Results in Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><pages><style face="normal" font="default" size="100%">103030</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper focuses on the degradation of water quality in the Mananga River and aims to analyze spatial changes for the recommendation for rehabilitation. The use of ArcGIS and JMP software allows for the assessment of water quality and the identification of the causes of the contaminants coming from probable sources that are near the river. These activities are mainly rural areas and businesses that could have affected the degradation of the quality of the water in the river. By generating spatial distribution maps and conducting rapid profiling, this paper provides recommendations for rehabilitation. The significance of this paper lies in contributing to decision-making processes and the formulation of protective laws that can help in stop the degradation of the water quality of the river. Limitation includes the quarterly frequency of the water monitoring of the river. The results of this paper suggest that the Mananga River does not meet the requirements for DENR Administrative Order 2016-08 for type A rivers. The spatial distribution maps generated were able to visualize and identify the anthropogenic activities that may be affecting the water quality of the river. The DO showed a value from 0.0011&amp;nbsp;mg/L – 18.800&amp;nbsp;mg/L, BOD has a value ranging from 1.000&amp;nbsp;mg/L to 81.500&amp;nbsp;mg/L, 4.000&amp;nbsp;mg/L – 325.980&amp;nbsp;mg/L for TSS, and pH showed values from 7.400 to 8.800. These findings emphasize the need for government intervention and the development of automated river monitoring systems to facilitate testing and rehabilitation efforts.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richard C. Cornelio</style></author><author><style face="normal" font="default" size="100%">Mayzonee V. Ligaray</style></author><author><style face="normal" font="default" size="100%">Tolentino B. Moya</style></author><author><style face="normal" font="default" size="100%">Cherry L. Ringor</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proof of concept for Bayesian inference of dynamic rating curve uncertainty in a sparsely gauged watershed</style></title><secondary-title><style face="normal" font="default" size="100%">Hydrological Sciences Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1080/02626667.2024.2401094</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">just-accepted</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Hydrometric data poverty compounds the challenge of accounting for uncertainties in non-stationary stage–discharge relationships. This paper builds on three methods to explore the integration of a dynamic approach to rating curve assessment and a physically based Bayesian framework for quantifying discharge amid geomorphologically induced rating shifts in a sparsely gauged alluvial river. The Modified GesDyn–FlowAM–BaRatin method entails sequentially segmenting gaugings according to residual indicators of riverbed instability and channel conveyance variability, leveraging cross-sectional surveys to augment calibration data, and eliciting hydraulic priors for probabilistic rating curve estimation. This method is applied to a Philippine watershed, where quarrying near the gauging station has ostensibly caused morphodynamic adjustments. Time-variable credible intervals for discharge are computed. The optimal estimates (RMSE = 2.96 m3/s) from maximum a posteriori rating curves outperform the hydrographer’s benchmark (RMSE = 5.00 m3/s), whose systematic errors from the gauged flows arise from lapses in shift detection.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jiyi Jang</style></author><author><style face="normal" font="default" size="100%">Sang-Soo Baek</style></author><author><style face="normal" font="default" size="100%">Daehyun Kang</style></author><author><style face="normal" font="default" size="100%">Yongeun Park</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Seungho Baek</style></author><author><style face="normal" font="default" size="100%">Jinyong Choi</style></author><author><style face="normal" font="default" size="100%">Bum Soo Park</style></author><author><style face="normal" font="default" size="100%">Myoung-In Lee</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Insights and machine learning predictions of harmful algal bloom in the East China Sea and Yellow Sea</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Cleaner Production</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0959652624019632</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">459</style></volume><pages><style face="normal" font="default" size="100%">142515</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The increase in harmful algal blooms (HABs) globally has been linked to climate change and anthropogenic activities such as agricultural runoff and urbanization. This study focused on analyzing the impact of these factors on HAB occurrences in the East China Sea and the Yellow Sea, identifying influential factors, and predicting future HAB events. For this study, random forest and numerical modeling were employed, with datasets encompassing physical and chemical properties of river water, seawater, and precipitation to assess the impact of discharge on HABs. Additionally, climate change scenarios derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were employed to predict future HAB occurrences, supported by a sensitivity analysis to identify influential factors affecting HAB occurrence. This study demonstrated that the growth rate and occurrence of HABs in the East China Sea (ECS) and Korean coastal waters (KCW) distinctively increased in July and November after the operation of the Three Gorges Dam (TGD). It is likely affected by the decreasing discharge from the Yangtze River (YR) owing to the operation of the TGD. Using the Random Forest model, future HAB events were predicted in good agreement with observations. The sensitivity results revealed that environmental properties, such as precipitation, water temperature, and salinity are major features affecting the HAB trends in both the KCW and YR basins. Moreover, based on the random forest model and climate change scenarios, HAB events were predicted to increase in frequency in July, September, and October. Therefore, the findings can contribute to preventing biological pollution of the ocean system in the ECS and KCW by supporting efficient environmental management.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mary Julia N. Mercado</style></author><author><style face="normal" font="default" size="100%">Wesly T. Cai</style></author><author><style face="normal" font="default" size="100%">Cybelle Concepcion M. Futalan</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Angelo Earvin Sy Choi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Water Quality Assessment of Mananga River Using Principal Component Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Philippine Journal of Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://philjournalsci.dost.gov.ph/accepted-articles/128-vol-153-no-2-april-2024/2065-water-quality-assessment-of-mananga-river-using-principal-component-analysis</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">153</style></volume><pages><style face="normal" font="default" size="100%">575-584</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Mananga River in the Philippines has been classified as a Class A river in 1997. Since then, the river has significantly deteriorated due to pollution, especially in the downstream area around Talisay in the Cebu region. In this study, the water quality parameters – namely, dissolved oxygen (DO), biological oxygen demand (BOD), and total suspended solids (TSS) were assessed using principal component analysis (PCA). Water quality data were obtained from the Department of Environmental and Natural Resources–Environmental Management Bureau Region 7. The water samples were collected on a quarterly basis from 2016–2019. The effect of weather conditions on water quality and significant relationships between the water quality parameters were determined. In addition, the comparison of water quality in each sampling station was investigated. Results show that about 84.3% of the total variance in water quality can be attributed to two significant principal component scores. Evident correlations were observed such as DO and BOD are negatively correlated, whereas a positive correlation exists between DO to RH and wind speed, BOD to temperature, and TSS to wind speed. Furthermore, negative correlations are observed between DO to temperature and wind direction, BOD to rainfall, and TSS to wind direction and rainfall. In the overall analysis of the results, the heavier the influence of a variable, the more likely it is to contribute to affecting the water quality. Therefore, this can alter the overall distribution within the plots and the correlations among the variables. The findings are a predictive measure of future changes and trends in the Mananga River. Therefore, the results of the present work will be used in environmental monitoring, environmental management, and assistance for the rehabilitation of the Mananga River using PCA.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">JongCheol Pyo</style></author><author><style face="normal" font="default" size="100%">Yakov Pachepsky</style></author><author><style face="normal" font="default" size="100%">Soobin Kim</style></author><author><style face="normal" font="default" size="100%">Ather Abbas</style></author><author><style face="normal" font="default" size="100%">Minjeong Kim</style></author><author><style face="normal" font="default" size="100%">Yongsung Kwon</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Long short-term memory models of water quality in inland water environments</style></title><secondary-title><style face="normal" font="default" size="100%">Water Research X</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.wroa.2023.100207</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">100207</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Soobin Kim</style></author><author><style face="normal" font="default" size="100%">Yongsung Kwon</style></author><author><style face="normal" font="default" size="100%">JongCheol Pyo</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Joong-Hyuk Min</style></author><author><style face="normal" font="default" size="100%">Jung Min Ahn</style></author><author><style face="normal" font="default" size="100%">Sang-Soo Baek</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Developing a cloud-based toolbox for sensitivity analysis of a water quality model</style></title><secondary-title><style face="normal" font="default" size="100%">Environmental Modelling &amp; Software</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.envsoft.2021.105068</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">105068</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The complexity associated with water&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/quality-model&quot; title=&quot;Learn more about quality models from ScienceDirect's AI-generated Topic Pages&quot;&gt;quality models&lt;/a&gt;&amp;nbsp;(WQMs) has increased owing to the introduction of numerous physical and biological mechanisms in the models. Sensitivity analysis (SA) is conducted to identify influential parameters in these mechanisms. However, enormous computational power and time are required to obtain numerical solutions from thousands of model simulations. Therefore, a cloud-based toolbox is developed for performing SA of WQMs by implementing a&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/cloud-computing-system&quot; title=&quot;Learn more about cloud computing system from ScienceDirect's AI-generated Topic Pages&quot;&gt;cloud computing system&lt;/a&gt;&amp;nbsp;using grab sampling data and&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/hyperspectral-image&quot; title=&quot;Learn more about hyperspectral images from ScienceDirect's AI-generated Topic Pages&quot;&gt;hyperspectral images&lt;/a&gt;&amp;nbsp;(HSI) of waterbodies.&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/cloud-computing&quot; title=&quot;Learn more about Cloud computing from ScienceDirect's AI-generated Topic Pages&quot;&gt;Cloud computing&lt;/a&gt;&amp;nbsp;can provide high-performance computation by adjusting the scale of the computational power according to user preference. The developed toolbox with the&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/cloud-system&quot; title=&quot;Learn more about cloud system from ScienceDirect's AI-generated Topic Pages&quot;&gt;cloud system&lt;/a&gt;&amp;nbsp;can reduce the computation time for SA by approximately 20 times compared to that of a desktop computer.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daeun Yun</style></author><author><style face="normal" font="default" size="100%">Ather Abbas</style></author><author><style face="normal" font="default" size="100%">Junho Jeon</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Sang-Soo Baek</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Developing a deep learning model for the simulation of micro-pollutants in a watershed</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Cleaner Production</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jclepro.2021.126858</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">300</style></volume><pages><style face="normal" font="default" size="100%">126858</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/detrimental-effect&quot; title=&quot;Learn more about detrimental effects from ScienceDirect's AI-generated Topic Pages&quot;&gt;detrimental effects&lt;/a&gt;&amp;nbsp;on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/deep-learning&quot; title=&quot;Learn more about deep learning from ScienceDirect's AI-generated Topic Pages&quot;&gt;deep learning&lt;/a&gt;&amp;nbsp;(DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative Nash–Sutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/pesticide-application&quot; title=&quot;Learn more about pesticide application from ScienceDirect's AI-generated Topic Pages&quot;&gt;pesticide application&lt;/a&gt;&amp;nbsp;increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/convolutional-neural-network&quot; title=&quot;Learn more about convolutional neural network from ScienceDirect's AI-generated Topic Pages&quot;&gt;convolutional neural network&lt;/a&gt;&amp;nbsp;(CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanghun Park</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Youngsik Kim</style></author><author><style face="normal" font="default" size="100%">Kangmin Chon</style></author><author><style face="normal" font="default" size="100%">Moon Son</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating the influence of catholyte salinity on seawater battery desalination</style></title><secondary-title><style face="normal" font="default" size="100%">Desalination</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.desal.2021.115018</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">506</style></volume><pages><style face="normal" font="default" size="100%">115018</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The seawater&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/battery-electrochemical-energy-engineering&quot; title=&quot;Learn more about battery from ScienceDirect's AI-generated Topic Pages&quot;&gt;battery&lt;/a&gt;&amp;nbsp;(SWB) is a promising&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/desalination&quot; title=&quot;Learn more about desalination from ScienceDirect's AI-generated Topic Pages&quot;&gt;desalination&lt;/a&gt;&amp;nbsp;technology that utilizes abundant&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/sodium-ion&quot; title=&quot;Learn more about sodium ions from ScienceDirect's AI-generated Topic Pages&quot;&gt;sodium ions&lt;/a&gt;&amp;nbsp;as an energy storage medium. Recently, the alternative&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/desalination-system&quot; title=&quot;Learn more about desalination system from ScienceDirect's AI-generated Topic Pages&quot;&gt;desalination system&lt;/a&gt;, seawater battery desalination (SWB-D), was developed by placing an SWB next to the desalination compartment. This SWB-D system can desalt water while charging the SWB next to it. However, only a fixed catholyte solution has been investigated, although the&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/catholytes&quot; title=&quot;Learn more about catholytes from ScienceDirect's AI-generated Topic Pages&quot;&gt;catholytes&lt;/a&gt;&amp;nbsp;impact the overall SWB-D performance. Therefore, we evaluated the effect of different catholytes on the desalination performance. High-saline&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/reverse-osmosis&quot; title=&quot;Learn more about reverse osmosis from ScienceDirect's AI-generated Topic Pages&quot;&gt;reverse osmosis&lt;/a&gt;&amp;nbsp;(RO) concentrate or brackish water exhibited excellent&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/chemical-engineering/salt-removal&quot; title=&quot;Learn more about salt removal from ScienceDirect's AI-generated Topic Pages&quot;&gt;salt removal&lt;/a&gt;&amp;nbsp;capability (&amp;gt;85.3% of sodium and &amp;gt;76.6% of chloride ions) with relatively short operation times (36.4&amp;nbsp;h for RO concentrate and 39.5&amp;nbsp;h for brackish water) upon charging, whereas the relatively low-saline river water showed the longest operation time (81.0&amp;nbsp;h), implying that river water should be excluded as a potential catholyte. The amount of desalinated water was marginally reduced due to osmosis through the&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/engineering/anion-exchange-membrane&quot; title=&quot;Learn more about anion exchange membrane from ScienceDirect's AI-generated Topic Pages&quot;&gt;anion exchange membrane&lt;/a&gt;; however, the amount of treated salt was &amp;gt;82.9% even after the reduction in water volume. These findings suggest that the catholyte with a resistance of &amp;gt;0.041&amp;nbsp;kΩ·cm can be ideal for the SWB-D.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Minjeong Kim</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Yongsung Kwon</style></author><author><style face="normal" font="default" size="100%">Soobin Kim</style></author><author><style face="normal" font="default" size="100%">Sangsoo Baek</style></author><author><style face="normal" font="default" size="100%">JongCheol Pyo</style></author><author><style face="normal" font="default" size="100%">Gahyun Baek</style></author><author><style face="normal" font="default" size="100%">Jingyeong Shin</style></author><author><style face="normal" font="default" size="100%">Jaai Kim</style></author><author><style face="normal" font="default" size="100%">Changsoo Lee</style></author><author><style face="normal" font="default" size="100%">Young Mo Kim</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Hazardous Materials</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jhazmat.2020.124587</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">409</style></volume><pages><style face="normal" font="default" size="100%">124587</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/marine-outfall&quot; title=&quot;Learn more about marine outfall from ScienceDirect's AI-generated Topic Pages&quot;&gt;marine outfall&lt;/a&gt;&amp;nbsp;can be a&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/wastewater-management&quot; title=&quot;Learn more about wastewater management from ScienceDirect's AI-generated Topic Pages&quot;&gt;wastewater management&lt;/a&gt;&amp;nbsp;system that discharges sewage and&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/stormwater&quot; title=&quot;Learn more about stormwater from ScienceDirect's AI-generated Topic Pages&quot;&gt;stormwater&lt;/a&gt;&amp;nbsp;into the sea; hence, it is a source of microbial pollution on recreational beaches, including antibiotic resistant genes (ARGs), which lead to an increase in untreatable diseases. In this regard, a marine outfall must be efficiently located to mitigate these risks. This study aimed to 1) investigate the spatiotemporal variability of&amp;nbsp;&lt;em&gt;Escherichia coli&lt;/em&gt;&amp;nbsp;(&lt;em&gt;E&lt;/em&gt;.&amp;nbsp;&lt;em&gt;coli&lt;/em&gt;) and ARGs on a recreational beach and 2) design marine outfalls to reduce microbial risks. For this purpose,&amp;nbsp;&lt;em&gt;E. coli&lt;/em&gt;&amp;nbsp;and ARGs with influential environmental variables were intensively monitored on Gwangalli beach, South Korea in this study.&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/environmental-fluid-dynamics&quot; title=&quot;Learn more about Environmental fluid dynamic from ScienceDirect's AI-generated Topic Pages&quot;&gt;Environmental fluid dynamic&lt;/a&gt;&amp;nbsp;code (EFDC) was used and calibrated using the monitoring data, and 12 outfall extension scenarios were explored (6 locations at 2 depths). The results revealed that repositioning the marine outfall can significantly reduce the concentrations of&amp;nbsp;&lt;em&gt;E. coli&lt;/em&gt;&amp;nbsp;and ARGs on the beach by 46–99%. Offshore extended outfalls at the bottom of the sea reduced concentrations of&amp;nbsp;&lt;em&gt;E. coli&lt;/em&gt;&amp;nbsp;and ARGs on the beach more effectively than onshore outfalls at the sea surface. These findings could be helpful in establishing microbial pollution management plans at recreational beaches in the future.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author><author><style face="normal" font="default" size="100%">Yakov Pachepsky</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Yongsung Kwon</style></author><author><style face="normal" font="default" size="100%">Kyung Hyun Kim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data assimilation in surface water quality modeling: A review</style></title><secondary-title><style face="normal" font="default" size="100%">Water Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.watres.2020.116307</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">186</style></volume><pages><style face="normal" font="default" size="100%">116307</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Data assimilation (DA) techniques are powerful means of dynamic natural system modeling that allow for the use of data as soon as it appears to improve model predictions and reduce prediction uncertainty by correcting state variables, model parameters, and boundary and initial conditions. The objectives of this review are to explore existing approaches and advances in DA applications for surface water quality modeling and to identify future research prospects. We first reviewed the DA methods used in water quality modeling as reported in literature. We then addressed observations and suggestions regarding various factors of DA performance, such as the mismatch between both lateral and vertical spatial detail of measurements and modeling, subgrid heterogeneity, presence of temporally stable spatial patterns in water quality parameters and related biases, evaluation of uncertainty in data and modeling results, mismatch between scales and schedules of data from multiple sources, selection of parameters to be updated along with state variables, update frequency and forecast skill. The review concludes with the outlook section that outlines current challenges and opportunities related to growing role of novel data sources, scale mismatch between model discretization and observation, structural uncertainty of models and conversion of measured to simulated vales, experimentation with DA prior to applications, using DA performance or model selection, the role of sensitivity analysis, and the expanding use of DA in water quality management.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ather Abbas</style></author><author><style face="normal" font="default" size="100%">Sangsoo Baek</style></author><author><style face="normal" font="default" size="100%">Minjeong Kim</style></author><author><style face="normal" font="default" size="100%">Mayzonee Ligaray</style></author><author><style face="normal" font="default" size="100%">Olivier Ribolzi</style></author><author><style face="normal" font="default" size="100%">Norbert Silvera</style></author><author><style face="normal" font="default" size="100%">Joong-Hyuk Min</style></author><author><style face="normal" font="default" size="100%">Laurie Boithias</style></author><author><style face="normal" font="default" size="100%">Kyung Hwa Cho</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Surface and sub-surface flow estimation at high temporal resolution using deep neural networks</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Hydrology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jhydrol.2020.125370</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">590</style></volume><pages><style face="normal" font="default" size="100%">125370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recent intensification in climate change have resulted in the rise of hydrological extreme events. This demands modeling of hydrological processes at high temporal resolution to better understand flow patterns in catchments. To model surface and sub-surface flows in a catchment we utilized a physically based model called Hydrological Simulated Program-FORTRAN and two deep learning-based models. One deep learning model consisted of only one long short-term memory (simple LSTM), whereas the other model simulated processes in each hydrological response unit (HRU) by defining one separate LSTM for each HRU (HRU-based LSTM). The models use environmental time-series data and two-dimensional spatial data to predict surface and sub-surface flows at 6-minute time step simultaneously. We tested our models in a tropical humid headwater catchment in northern Lao PDR and compared their performances. Our results showed that the simple LSTM model outperformed the other models on surface runoff prediction with the lowest MSE (7.4e−5&amp;nbsp;m3&amp;nbsp;s−1), whereas HRU-based LSTM model better predicted patterns and slopes in sub-surface flow in comparison with the other models by having the smallest MSE value (3.2e−4&amp;nbsp;m3&amp;nbsp;s−1). This study demonstrated the performance of a deep learning model when simulating hydrological cycle with high temporal resolution.</style></abstract></record></records></xml>