서울시립대 - 차윤경 물환경관리연구실

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Journals

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34. Explainable machine learning for diagnosing ecological impairment in streams using reach-scale biological and environmental data

Taeseung Park, Se-Rin Park, Jihoon Shin, Hyunji Kim, Kyung-A You, Sang-Woo Lee, YoonKyung Cha

Ecological Informatics Volume 96, June 2026, 103852 Link
33. System-level total nitrogen prediction in MLE wastewater treatment using a modular merged LSTM framework

Taehyeon Kim, Jaegwan Park, Yoojin Oh, Haekeum Park, Jayong Koo, Mincheol Kim, Yoonkyung Cha

Journal of Water Process Engineering Volume 81 Link
32. Development of a self-supervised deep learning framework for chlorophyll-a retrieval in data-scarce inland waters

Bongseok Jeong, Jihoon Shin, YoonKyung Cha

Environmental Modelling & Software Volume 197, February 2026, 106817 Link
31. A river network model using a weight-based merged LSTM for multi-source monitoring integration

Jonggyu Jung, Taeseung Park, Jaegwan Park, Dogeon Lee, YoonKyung Cha

Ecological Informatics Volume 90, December 2025, 103320 Link
30. Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems

Jaegwan Park, Taeseung Park, Dogeon Lee, Jihoon Shin, Kyunghyun Kim, Jonggyu Jung, Hongtae Kim, Yoonkyung Cha

Ecological Informatics Volume 92, December 2025, 103481 Link
29. Deep learning-based prediction of exceeding the criteria for river chlorophyll a concentrations using high-frequency data from a sensor network

Gunhyeong Lee, Jihoon Shin, Young Woo Kim, Eun Jin Han, Chung Seok Yu, Taeho Kim, YoonKyung Cha

Environmental Engineering Research 2025; 30(5): 240302 Link
28. A modular deep learning surrogate model for simulating harmful algal blooms in complex process-based systems

Young Woo Kim, Yoonkyung Cha, Jihoon Shin

Water Research Volume 285, 1 October 2025, 124059 Link
27. Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products

Taeho Kim, HaeDeun Lee, SooHyun Yang, GunHyeong Lee, Jihoon Shin, YoonKyung Cha

Harmful Algae, Volume 148, 102896 Link
26. Modeling ecosystem-wide responses to environmental stressors: A multi-trophic hierarchical Bayesian network approach

Taeseung Park, Jaegwan Park, Dogeon Lee, Jonggyu Jung, Geumbit Hwang, Jeongsuk Moon, Hyun-Han Kwon, YoonKyung Cha

Journal of Environmental Management Volume 391, September 2025, 126480 Link
25. Synthetic data-augmented machine learning approaches for tailor-made microbial conversion of methane to phytoene

Chang Keun Kang, Jihoon Shin, Min Sun Kim, Min Sun Choi, YoonKyung Cha, Yong Jun Choi

Bioresource Technology Volume 437, 133160 Link
24. Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model

Pamela Sofia Fabian, YoonKyung Cha, Kyung-A You, Hyun-Han Kwon

Chemical Engineering Journal Volume 514, 163074 Link
23. The analysis of spatiotemporal effects of environmental factors on harmful algal blooms in a bloom-prone river using partial least squares structural equation modeling

Bongseok Jeong, Hyunjoo Shin, Jihoon Shin, YoonKyung Cha

Water Science & Technology Volume 91, 10, 1128-1140 Link
22. 다변량 통계분석 기반 저서성 대형무척추동물지수 등급체계 평가

이도건, 공동수, 박배경, 박성애, 차윤경

한국물환경학회지 vol.41, no.1, pp. 18-29 (12 pages) Link
21. Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Jihoon Shin , YoonKyung Cha

Water Research Volume 268, Part B, 1 January 2025, 122751 Link
20. Generalizability evaluations of heterogeneous ensembles for river health predictions

Taeseung Park, Jihoon Shin, Baekyung Park, Jeongsuk Moon, Yoonkyung Cha

Ecological Informatics Volume 82, September 2024, 102719 Link

2026

Explainable machine learning for diagnosing ecological impairment in streams using reach-scale biological and environmental data

Taeseung Park, Se-Rin Park, Jihoon Shin, Hyunji Kim, Kyung-A You, Sang-Woo Lee, YoonKyung Cha

Ecological Informatics Volume 96, June 2026, 103852 Link
System-level total nitrogen prediction in MLE wastewater treatment using a modular merged LSTM framework

Taehyeon Kim, Jaegwan Park, Yoojin Oh, Haekeum Park, Jayong Koo, Mincheol Kim, Yoonkyung Cha

Journal of Water Process Engineering Volume 81 Link

2025

Development of a self-supervised deep learning framework for chlorophyll-a retrieval in data-scarce inland waters

Bongseok Jeong, Jihoon Shin, YoonKyung Cha

Environmental Modelling & Software Volume 197, February 2026, 106817 Link
A river network model using a weight-based merged LSTM for multi-source monitoring integration

Jonggyu Jung, Taeseung Park, Jaegwan Park, Dogeon Lee, YoonKyung Cha

Ecological Informatics Volume 90, December 2025, 103320 Link
Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems

Jaegwan Park, Taeseung Park, Dogeon Lee, Jihoon Shin, Kyunghyun Kim, Jonggyu Jung, Hongtae Kim, Yoonkyung Cha

Ecological Informatics Volume 92, December 2025, 103481 Link
Deep learning-based prediction of exceeding the criteria for river chlorophyll a concentrations using high-frequency data from a sensor network

Gunhyeong Lee, Jihoon Shin, Young Woo Kim, Eun Jin Han, Chung Seok Yu, Taeho Kim, YoonKyung Cha

Environmental Engineering Research 2025; 30(5): 240302 Link
A modular deep learning surrogate model for simulating harmful algal blooms in complex process-based systems

Young Woo Kim, Yoonkyung Cha, Jihoon Shin

Water Research Volume 285, 1 October 2025, 124059 Link
Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products

Taeho Kim, HaeDeun Lee, SooHyun Yang, GunHyeong Lee, Jihoon Shin, YoonKyung Cha

Harmful Algae, Volume 148, 102896 Link
Modeling ecosystem-wide responses to environmental stressors: A multi-trophic hierarchical Bayesian network approach

Taeseung Park, Jaegwan Park, Dogeon Lee, Jonggyu Jung, Geumbit Hwang, Jeongsuk Moon, Hyun-Han Kwon, YoonKyung Cha

Journal of Environmental Management Volume 391, September 2025, 126480 Link
Synthetic data-augmented machine learning approaches for tailor-made microbial conversion of methane to phytoene

Chang Keun Kang, Jihoon Shin, Min Sun Kim, Min Sun Choi, YoonKyung Cha, Yong Jun Choi

Bioresource Technology Volume 437, 133160 Link
Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model

Pamela Sofia Fabian, YoonKyung Cha, Kyung-A You, Hyun-Han Kwon

Chemical Engineering Journal Volume 514, 163074 Link
The analysis of spatiotemporal effects of environmental factors on harmful algal blooms in a bloom-prone river using partial least squares structural equation modeling

Bongseok Jeong, Hyunjoo Shin, Jihoon Shin, YoonKyung Cha

Water Science & Technology Volume 91, 10, 1128-1140 Link
다변량 통계분석 기반 저서성 대형무척추동물지수 등급체계 평가

이도건, 공동수, 박배경, 박성애, 차윤경

한국물환경학회지 vol.41, no.1, pp. 18-29 (12 pages) Link
Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Jihoon Shin , YoonKyung Cha

Water Research Volume 268, Part B, 1 January 2025, 122751 Link

2024

Generalizability evaluations of heterogeneous ensembles for river health predictions

Taeseung Park, Jihoon Shin, Baekyung Park, Jeongsuk Moon, Yoonkyung Cha

Ecological Informatics Volume 82, September 2024, 102719 Link

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Publications

Journals

34. Explainable machine learning for diagnosing ecological impairment in streams using reach-scale biological and environmental data

33. System-level total nitrogen prediction in MLE wastewater treatment using a modular merged LSTM framework

32. Development of a self-supervised deep learning framework for chlorophyll-a retrieval in data-scarce inland waters

31. A river network model using a weight-based merged LSTM for multi-source monitoring integration

30. Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems

29. Deep learning-based prediction of exceeding the criteria for river chlorophyll a concentrations using high-frequency data from a sensor network

28. A modular deep learning surrogate model for simulating harmful algal blooms in complex process-based systems

27. Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products

26. Modeling ecosystem-wide responses to environmental stressors: A multi-trophic hierarchical Bayesian network approach

25. Synthetic data-augmented machine learning approaches for tailor-made microbial conversion of methane to phytoene

24. Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model

23. The analysis of spatiotemporal effects of environmental factors on harmful algal blooms in a bloom-prone river using partial least squares structural equation modeling

22. 다변량 통계분석 기반 저서성 대형무척추동물지수 등급체계 평가

21. Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

20. Generalizability evaluations of heterogeneous ensembles for river health predictions

2026

Explainable machine learning for diagnosing ecological impairment in streams using reach-scale biological and environmental data

System-level total nitrogen prediction in MLE wastewater treatment using a modular merged LSTM framework

2025

Development of a self-supervised deep learning framework for chlorophyll-a retrieval in data-scarce inland waters

A river network model using a weight-based merged LSTM for multi-source monitoring integration

Generalizable deep learning forecasting of harmful algal blooms using transfer learning across river systems

Deep learning-based prediction of exceeding the criteria for river chlorophyll a concentrations using high-frequency data from a sensor network

A modular deep learning surrogate model for simulating harmful algal blooms in complex process-based systems

Long-term spatiotemporal variability and regime classification of Chlorophyll-a concentrations in Lake Erie using satellite products

Modeling ecosystem-wide responses to environmental stressors: A multi-trophic hierarchical Bayesian network approach

Synthetic data-augmented machine learning approaches for tailor-made microbial conversion of methane to phytoene

Spatiotemporal dynamics of summer chlorophyll-a concentrations under varying drought conditions in a hierarchical Bayesian model

The analysis of spatiotemporal effects of environmental factors on harmful algal blooms in a bloom-prone river using partial least squares structural equation modeling

다변량 통계분석 기반 저서성 대형무척추동물지수 등급체계 평가

Development of a deep learning–based feature stream network for forecasting riverine harmful algal blooms from a network perspective

2024

Generalizability evaluations of heterogeneous ensembles for river health predictions

논문정보