Sang-Soo Baek

发表

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

O. Ribolzi, K. Cho, L. Boithias, 2017 .

Watershed-scale modeling on the fate and transport of polycyclic aromatic hydrocarbons (PAHs).

K. Cho, Mayzonee Ligaray, Sang-Soo Baek, 2016, Journal of hazardous materials.

Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approach.

Hyuk Lee, K. Cho, Sang-Soo Baek, 2015, Water research.

Replacing the internal standard to estimate micropollutants using deep and machine learning.

Jongkwan Park, K. Cho, Sang-Soo Baek, 2020, Water research.

Surface and sub-surface flow estimation at high temporal resolution using deep neural networks

O. Ribolzi, N. Silvera, K. Cho, 2020 .

Assessment of a green roof practice using the coupled SWMM and HYDRUS models.

Y. Pachepsky, K. Cho, J. Chun, 2020, Journal of environmental management.

Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments

Y. Her, K. Cho, Sang-Soo Baek, 2019, Journal of Hydrology.

A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

M. Kim, Hyuk Lee, K. Cho, 2019, Remote Sensing of Environment.

Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration

Y. Pachepsky, Jongkwan Park, K. Cho, 2019, Journal of Membrane Science.

Chemical accidents in freshwater: Development of forecasting system for drinking water resources.

K. Cho, S. D. Kim, Minjeong Kim, 2022, Journal of hazardous materials.

Optimizing Agricultural Best Management Practices in a Lake Erie Watershed

Kyung Hwa Cho, Sang-Soo Baek, JongCheol Pyo, 2017 .

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level.

Y. Pachepsky, K. Cho, Sang-Soo Baek, 2022, Water research.

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

J. Chun, Sang-Soo Baek, J. Pyo, 2020 .

Identification and enumeration of cyanobacteria species using a deep neural network

Y. Pachepsky, Yongeun Park, Mayzonee Ligaray, 2020 .

Potential inhibition of the harmful dinoflagellate Cochlodinium (= Margalefidinium) polykrikoides by the intrusion of Changjiang diluted water into Korea coastal waters

Bum Soo Park, Sang-Soo Baek, S. Baek, 2022, Ecological Indicators.

Using convolutional neural network for predicting cyanobacteria concentrations in river water.

Y. Pachepsky, K. Cho, Kyunghyun Kim, 2020, Water research.

Hydrometeorological Influence on Antibiotic-Resistance Genes (ARGs) and Bacterial Community at a Recreational Beach in Korea.

K. Cho, Minjeong Kim, Sang-Soo Baek, 2020, Journal of hazardous materials.

A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds

Y. Pachepsky, K. Cho, J. Chun, 2020 .

Modeling of NF/RO membrane fouling and flux decline using real-time observations

Jongkwan Park, K. Cho, Kwanho Jeong, 2019, Journal of Membrane Science.

Investigating Influence of Hydrological Regime on Organic Matters Characteristic in a Korean Watershed

Jongkwan Park, Hyuk Lee, K. Cho, 2019, Water.

Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation.

K. Cho, Sang-Soo Baek, J. Pyo, 2021, Harmful algae.

Unique microbial module regulates the harmful algal bloom (Cochlodinium polykrikoides) and shifts the microbial community along the Southern Coast of Korea.

K. Cho, H. Oh, Sang-Soo Baek, 2020, The Science of the total environment.

Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model

Yakov A. Pachepsky, Hyuk Lee, Kyung Hwa Cho, 2017, Environ. Model. Softw..

Evaluation of a hydrology and run-off BMP model in SUSTAIN on a commercial area and a public park in South Korea

Kyung Hwa Cho, Jae-Woon Jung, Kwang-Sik Yoon, 2015 .

Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

K. Cho, H. Oh, Sang-Soo Baek, 2021, Frontiers in Marine Science.

Developing a cloud-based toolbox for sensitivity analysis of a water quality model

Kyung Hwa Cho, Jung Min Ahn, Sang-Soo Baek, 2021, Environ. Model. Softw..

A novel method for micropollutant quantification using deep learning and multi-objective optimization.

Daeho Kang, K. Cho, Sang-Soo Baek, 2022, Water research.

Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.

K. Cho, Joo-Hyon Kang, Yongeun Park, 2021, Water research.

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Kyung Hwa Cho, Sang-Soo Baek, Mayzonee Ligaray, 2015 .

Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water

Jae-Hyun Ahn, Kyung Hwa Cho, Sang-Soo Baek, 2021, Remote. Sens..

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

K. Cho, Sang-Soo Baek, J. Pyo, 2022, Environmental Engineering Research.

A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.

K. Cho, Sunghwan Kim, Yongeun Park, 2021, Journal of environmental management.

Supplementary material to "In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models"

Y. Pachepsky, O. Ribolzi, N. Silvera, 2021, Hydrology and Earth System Sciences.

Developing a deep learning model for the simulation of micro-pollutants in a watershed

K. Cho, Mayzonee Ligaray, Sang-Soo Baek, 2021, Journal of Cleaner Production.

Developing a hydrological simulation tool to design bioretention in a watershed

Hyun-Suk Shin, Yongsung Kwon, Kyung Hwa Cho, 2017, Environ. Model. Softw..

Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling.

K. Cho, Mayzonee Ligaray, Minjeong Kim, 2020, Journal of hazardous materials.

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyuk Lee, Kyunghyun Kim, Kyung Hwa Cho, 2020, Remote. Sens..

Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning.

Daeho Kang, K. Cho, Sang-Soo Baek, 2023, Water research.

Effect of marine heatwaves on bloom formation of the harmful dinoflagellate Cochlodinium polykrikoides: Two sides of the same coin?

Bum Soo Park, J. H. Kim, Sang-Soo Baek, 2021, Harmful algae.

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage.

Sanghyung Park, Gibeom Nam, Kyunghyun Kim, 2021, Water research.

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

Sanghyun Park, Hyuk Lee, Minjeong Kim, 2017, Remote. Sens..

Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Sang-Soo Baek, Minhyuk Jeung, Jiyi Jang, 2023, Journal of Hydrology.

In-stream Escherichia Coli Modeling Using high-

Y. Pachepsky, O. Ribolzi, N. Silvera, 2021 .

Developing a data-driven modeling framework for simulating a chemical accident in freshwater

Sang-Soo Baek, Ather Abbas, Soobin Kim, 2023, Journal of Cleaner Production.

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

Sang-Soo Baek, J. Pyo, Soobin Kim, 2024, Water research X.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry.

Sang-Soo Baek, Junho Jeon, Daeun Yun, 2024, Chemosphere.