[60] J. Jang, J.G. Moon, S.J. Jung, S.M. Suh, H.S. La*, J.C. Pyo*, Classification of artic sounds using deep learning model with spectrogram-based audio mapping (In preparation)
[59] H. Jeong, D. Yun, S.-S Baek, J.C. Pyo, D. Kang, J. Jeon, K.H. Cho, Multimodal deep learning for micropollutant prediction in urban and agricultural watersheds (Submitted)
[58] A. Abbas, Y. Yang, M. Pan, Y. Tramblay, C. Shen, H. Ji, S.H. Gebrechorkos, F. Pappenberger, J.C. Pyo, D. Feng, G. Huffman, P.D. Nguyen, C. Massari, L. Brocca, H.E. Beck.Comprehensive global assessment of 23 gridded precipitation datasets across 16,295 catchments using hydrological modeling (Submitted)
[57] J.G. Moon, S.J. Jung, S.M. Suh, J.C. Pyo*, Development of deep learning quantization framework for remote sensing edge device to estiate inland water quality in South Korea (Submitted)
[56] S.J. Jung, J.G. Moon, S.M. Suh, J.C. Pyo* Influence of weir retention time on data-driven models for predicting algal bloom (Submitted)
[55] K. Phetanan, D.H. Kwon, J. Lee, H. Jeong, G. Nam, E. Hwang, J.C. Pyo*, K.H. Cho* SAR remote sensing for monitoring harmful algal blooms using deep learning models (Submitted)
[54] J.C. Pyo, S.-S. Baek, A. Abbas, H.G. Kim, J. Lee, S. Kim, J. A. Chun, K.H. Cho Improving reservoir water quality by optimizing weir operations with reinforcement learning and SWAT (Submitted)
[53] D.H. Kwon, S. Lee, J. Lee, J.C. Pyo, A. Abbas, S. Park, K. Kim, H. Lee, K.H. Cho Probabilistic machine larning-based phytoplankton abundance prediction using hyperspectral remote sensing (In revision)
[52] O.D. Ekpe, H. Moon, J.C. Pyo, J.-E. Oh (2025) Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model, Water Research, 270, 122824
[51] S.M. Suh, J.G. Moon, S.J. Jung, J.C. Pyo* (2024) Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea, Science of the Total Environment, 957, 177459.
[50] D. Lee, J.G. Moon, S.J. Jung, S.M. Suh, J.C. Pyo* (2024) Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique, Science of the Total Environment, 954, 176585.
[49] J.G. Moon, S.M. Suh, S.J. Jung, S.-S. Baek, J.C. Pyo* (2024) Deep learnig-based mapping of total suspended solids in rivers across South Korea using high resolution satellite imagery, GIScience & Remote Sensing, 61(1), 2393489
[48] J. Kim, J. Kim, W. Jang, H. Lee, J.C. Pyo, S. Byeon, H. Lee, Y. Park, S. Kim (2024) Enhancing machine learning performance in estimating CDOM absorption coefficient via data resampling, Remote Sensing, 16(13), 2313
[47] C. Esu, J.C. Pyo, K. Cho (2024) Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter, Journal of Hazardous Materials, 475, 134864
[46] S. Kim, E. Lee, H.-T Hwang, J.C. Pyo, D. Yun, S.-S. Baek, K.H. Cho (2024) Spatiotemporal estimation of groundwater and surface water conditions integrating deep learning and physics-based watershed models. Water Research X, 23, 100228
[45] Y.S. Kwon, H. Kang, J.C. Pyo* (2024) Estimation of aquatic ecosystem health using deep neural network with nonlinear data mapping. Ecological Informatics, 81, 102588.
[44] J. Jang, S. Kim, S.M. Cha, J.C. Pyo, K.-S. Yoon, H. Lee, Y. Park, I.-S. Jang, K.-J. Kim, J.-W. Yu, M.-S. Kang, H.-S. Bae, S.-S. Baek, K.H. Cho (2024) Simulations of low impact development designs using the storm water management model. Environmental Engineering Research, 29(6), 230712
[43] J. Shin, G. Lee, T.H. Kim, K.H. Cho, S.M. Hong, D.H. Kwon, J.C. Pyo, Y.K. Cha (2024) Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach. Science of the Total Environment, 912, 169540
[42] J.C. Pyo, Y. Pachepsky, S. Kim, A. Abbas, M. Kim, Y.S. Kwon, M. Ligaray, K.H. Cho (2023) Long short-term memory models of water quality in freshwater environment. Water Research X, 21, 100207.
[41] S. Kim, A. Abbas, J.C. Pyo, H. Kim, S.M. Hong S.-S. Baek, K.H. Cho (2023) Developing a data-driven modeling framework for simulating a chemical accident in freshwater. Journal of Cleaner Production, 425(1), 138842.
[40] D.H. Kwon, S.M. Hong, A. Abbas, S. Park, G. Nam, J.-H. Yoo, K. Kim, H.T. Kim, J.C. Pyo*, K.H. Cho* (2023) Deep Learning-based Super-Resolution for Harmful Algal Bloom monitoring of Inland Water, GIScience & Remote Sensing, 60 (1), 2249753.
[39] S.M. Hong, A. Abbas, S. Kim, D. H. Kwon, N. Yoon, D. Yun, S. Lee, Y. Pachepsky, J.C. Pyo*, K.H. Cho* (2023). Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system. Environmental Modelling & Software, 105805.
[38] J.C. Pyo, K.J. Han, Y. Cho, D. Kim, D.Y. Jin (2022) Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery, Forest, 13(12), 2170.
[37] J. Kim, W. Jang, J. H. Kim, J. Lee, K. H. Cho, Y. G. Lee, K. Chon, S. Park, J.C. Pyo, Y. Park, Kim, S. (2022). Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir. International Journal of Applied Earth Observation and Geoinformation, 114, 103053.
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[36] D.H. Kwon, S.M. Hong, A. Abbas, J.C. Pyo, H.-K. Lee, S.-S. Baek, K.H. Cho (2023) Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning, Environmental Engineering Research, 28(1), 210280.
[35] S.-S. Baek, E.-Y. Jung, J.C. Pyo, Y. Pachepsky, H.-J. Son, K.H. Cbo (2022) Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level, Water Research, 218, 118494.
[34] W. Jang, Y. Park, J.C. Pyo, S. Park, J. Kim, J. Kim, K.H. Cho, J.-K. Shin, S. Kim (2022) Optimal band selection for airborne hyperspectral imagery to retrieve a wide range of cyanobacteria pigment concentration using data-driven model, Remote Sensing, 14(7), 1754.
[33] S.M. Hong, K.H. Cho, S. Park, Taegu Kang, G. Nam*, J.C. Pyo* (2022) Estimation of cyanobacteria using spatial attention convolutional neural network and hyperspectral images in main streams of South Korea, GIScience and Remote Sensing 59(1), 547-567.
[32] J. Lee, S.-Y. Woo, Y.-W. Kim, S.-J. Kim, J.C. Pyo*, K.H. Cho* (2022) Dynamic calibration of phytoplankton blooms using the modified SWAT model, Journal of Cleaner Production 343, 118080.
[31] D. Yun, J, Jang, D. Kang, A.T. Angeles, J.C. Pyo, J. Jeon, S.-S. Baek, K.H. Cho (2022) A novel method for micropollutant quantification using deep learning and multi-objective optimization, Water Research, 212, 118080.
[30] J.C. Pyo, S.M. Hong, J. Jang, S. Park, J. Park, J.H. Noh, K.H. Cho (2022) Drone-borne sensing of major and accessory pigments in algae using deep learning modeling, GIScience and Remote Sensing, 59(1), 310-332.
[29] S.-S. Baek, D. Yun, J.C. Pyo, D. Kang, J. Jeon, K.H. Cho (2021) Identification of micropollutants in marine outfall using network analysis and machine learning, Science of the Total Environment, 150938.
[28] S.-S Baek, J.C. Pyo, Y.S. Kwon, S.-J. Chun, S. Baek, C.-Y. Ahn, H.M. Oh, Y.O. Kim, K.H. Cho (2021) Deep learning for simulating harmful algal blooms using ocean numerical model, Frontiers in Marine Science, 8, 1446.
[27] J.C. Pyo, K.H. Cho, K.H. Kim, S.-S. Baek, G. Nam, S. Park (2021) Cyanobacteria cell prediction using interpretable deep learning model with observed, synthetic, and sensing data assemblage, Water Research, 203, 117483.
[26] Q.V. Ly, N.C. Le, X.C. Nguyen, T.-D. Truong, T.-H.T. Hoang, T.J. Park, X. Yang, J.C. Pyo, K.H. Cho, J. Hur (2021) Application of machine learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea, Science of the Total environment, 797, 149040.
[25] S.M. Hong, S.-S. Baek, D. Yun, Y. H. Kwon, J.C. Pyo*, K. H. Cho* (2021) Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models, Science of the Total Environment, 794, 148592.
[24] J.C. Pyo, J. Min, G. Nam, Y.-S. Song, J. M. Ahn, S. Park, J. Lee, K.H. Cho, Y. Park (2021) Effect of hyperspectral image-based initial condition on improving algal simulation of hydrodynamic and water quality model, Journal of Environmental Management, 294, 112988.
[23] S. Kim, Y.S. Kwon, J.C. Pyo, S.-S Baek, J Min, J.-M. Ahn, K.H. Cho (2021) Developing a cloud-based toolbox for sensitivity analysis of a water quality model, Environmental Modeling & Software, 105068.
[22] S.-S Baek, Y.S. Kwon, J.C. Pyo, Y.-O. Kim, K.H. Cho (2021) Identification of the influence factor for Alexandrium catenella bloom using machine learning and numerical simulation approach, Harmful Algae, 103, 102007.
[21] J.C. Pyo, Y.S. Kwon, J.-H. Ahn, S.-S. Baek, K.H. Cho (2021) Sensitivity analysis and optimization of a radiative transfer numerical model for turbid lake water, Remote Sensing, 13(4), 709
[20] S.-S. Baek, Y. Choi, J. Jeon, J.C. Pyo, J. Park, K.H. Cho (2021) Estimation of micro-pollutant concentration using deep learning and high-resolution mass spectrometry, Water Research, 188, 116535.
[19] M. Kim, M. Ligaray, Y.S. Kwon, S. Kim, S.-S. Baek, J.C. Pyo, G. Baek, J. Shin, J. Kim, C. Lee, Y.M. Kim, K.H. Cho (2021) Designing a marine outfall to reduce microbial risk on a recreational beach: field experiment and modeling, Journal of Hazardous Materials.
[18] S.-S, Baek, J.C. Pyo, J. A. Chun (2020) Prediction of water level and water quality using a CNN-LSTM linked deep learning approach, Water, 12(12), 3399.
[17] J.C. Pyo, L.J. Park, Y. Pachepsky, S.-S. Beak, K. Kim, K.H. Cho (2020) Using convolutional neural network for predicting cyanobacteria in river water, Water Research, 186(1), 116349.
[16] J.C. Pyo, S.M. Hong, Y.S. Kwon, M.S. Kim, K.H. Cho (2020) Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil, Science of the Total environment, 741, 140162.
[15] S.-S Baek, J.C. Pyo, Y. Pachepsky, Y. Park, C.-Y. Ahn, Y.-H. Kim, J.A. Chun, K.H. Cho (2020) Identification and enumeration of cyanobacteria species with the deep neural network, Ecological Indicators, 115, 106395.
[14] S.-S. Baek, M. Ligaray, J.C. Pyo, J.-P. Park, J.-H. Kang, J.A. Chun, K.H. Cho (2020) A novel water quality module of the SWMM model for assessing Low Impact Development (LID) in urban watersheds, Journal of Hydrology, 586, 124886.
[13] J.C. Pyo, H. Duan, M. Ligaray, M. Kim, S.-S. Baek, Y.S. Kwon, H. Lee, T. Kang, K. Kim, Y.K. Cha, K.H. Cho (2020) An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery, Remote Sensing, 12(7), 1073.
[12] Y.S. Kwon, J.C. Pyo, Y.-H. Kwon, H. Duan, K.H. Cho, Y. Park (2020) Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir, Remote Sensing of Environment, 236, 111517.
[11] J.C. Pyo, Y. Pachepsky, M. Kim, S.-S. Baek, H. Lee, Y.K. Cha, K.H. Cho, Y. Park (2019) Simulating seasonal variability of phytoplankton in stream water using the modified SWAT model, Environmental Modelling & Software, 122, 104073.
[10] J.C. Pyo, H. Duan, S.-S. Baek, M.S. Kim, T. Jeon, Y.S. Kwon, H. Lee, K.H. Cho (2019) A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery, Remote Sensing of Environment, 233, 111350.
[9] S. Park, S.-S. Baek, J.C. Pyo, Y. Pachepsky, J. Park, K.H. Cho (2019) Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration, Journal of Membrane Science, 587(1), 117164.
[8] Y. S. Kwon, S.H. Baek, Y.K. Lim, J.C. Pyo, M. Ligaray, Y. Park, K.H. Cho (2018) Monitoring coastal chlorophyll-a concentrations in coastal areas using machine learning models, Water, 10, 1020.
[7] J.C. Pyo, Y.S. Kwon, M.-H. Ahn, K. Kim, H. Lee, T. Kang, S.B. Cho, Y. Park, K.H. Cho (2018) High-spatial resolution monitoring of phycocyanin and chlorophyll-a using airborne hyperspectral imagery, Remote Sensing, 10, 1180.
[6] Y. Park, J.C. Pyo, Y.S. Kwon, Y.K. Cha, H. Lee, T. Kang, K.H. Cho (2017) Evaluating physio-chemical influences on cyanobacterial blooms using hyperspectral image in inland water, Korea, Water Research, 126, 319-328.
[5] J.C. Pyo, S.-S. Baek, M. Kim, S. Park, H. Lee, K.H. Cho (2017). Optimizing agricultural best management practices in a Lake Erie watershed, Journal of the American Water Resources Association, 53(6), 1281-1292.
[4] J.C. Pyo, Y. Pachepsky, S.-S. Baek, Y.S. Kwon, M. Kim, H. Lee, S. Park, Y.K. Cha, R. Ha, G. Nam, Y. Park, K.H. Cho (2017) Optimizing semi-analytical algorithms for estimating chlorophyll-a and phycocyanin concentrations in inland waters in Korea, Remote Sensing, 9, 542.
[3] K.H. Cho, Y. Pachepsky, M. Kim, J.C. Pyo, M.-H. Park, J.-W. Kim, Y.M. Kim, J.H. Kim (2016) Modeling seasonal variability fecal coliform in natural surface waters using the modified SWAT, Journal of Hydrology, 535, 377-385.
[2] J.C. Pyo, S. Ha, Y. Pachepsky, H. Lee, R. Ha, G. Nam, M.S. Kim, J. Im, K.H. Cho (2016) Chlorophyll-a concentrations estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea, Remote Sensing Letters, 7(5), 407-416.
[1] M. Kim, S.-S Baek, M. Ligaray, J.C. Pyo, M. Park, K.H. Cho (2015) Comparative studies of different imputation methods for recovering streamflow observation, Water, 7(12), 6847-6860.