서울시립대 - 차윤경 물환경관리연구실

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Publications
Journals

Journals

일자별
년도별

19. Multisite algal bloom predictions in a lake using graph attention networks

Nakgyeom Kim, Jihoon Shin, YoonKyung Cha

Environmental Engineering Research 2024; 29(2): 230210. Published online: June 13, 2023 Link
18. Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

Jihoon Shin, Gunhyeong Lee, Taeho Kim, Kyung Hwa Cho, Seok Min Hong, Do Hyuck Kwon, Jongcheol Pyo, Yoonkyung Cha

Science of The Total Environment Volume 912, 20 February 2024, 169540 Link
17. Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

Hyo Gyeom Kim, YoonKyung Cha, Kyung Hwa Cho

Water Research Volume 249, 1 February 2024, 120928 Link
16. Changes in zooplankton community in response to a shift from lentic to lotic conditions in a regulated river

Taeseung Park, Gunhyeong Lee, Jihoon Shin, Jong-hwan Park, Byungwoong Choi, Dong-Kyun Kim, YoonKyung Cha

Ecological Informatics Volume 77, November 2023, 102236 Link
15. Data-driven models for predicting community changes in freshwater ecosystems: A review

Da-Yeong Lee, Dae-Seong Lee, YoonKyung Cha, Joong-Hyuk Min, Young-Seuk Park

Ecological Informatics Volume 77, November 2023, 102163 Link
14. Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

TaeHo Kim, Jihoon Shin, YoonKyung Cha

Journal of Cleaner Production Volume 414, 15 August 2023, 137564 Link
13. Hybrid model for daily streamflow and phosphorus load prediction

DoYeon Lee, Jihoon Shin, TaeHo Kim, Sangchul Lee, Dongho Kim, Yeonjeong Park, YoonKyung Cha

Water Sci Technol (2023) 88 (4): 975–990. Link
12. Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Yongsung Kwon, YoonKyung Cha, Yeonjeong Park, Sangchul Lee

Scientific Reports volume 13, Article number: 9296 (2023) Link
11. 학습 기반 최적화 방법론을 이용한 서울시 도로변대기측정망 확장

김태호, 신지훈, 김영우, 이도연, 백주은, 이도윤, 차윤경

Journal of Korean Society of Environmental Engineers 2023; 45(3): 148-160. Published online: March 31, 2023 Link
10. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

Chang Keun Kang, Jihoon Shin, YoonKyung Cha, Min Sun Kim, Min Sun Choi, TaeHo Kim, Young-Kwon Park, Yong Jun Choi

Bioresource Technology Volume 369, 128455 Link
9. Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Jihoon Shin, SangHyun Son, YoonKyung Cha

Sustainable Cities and Society Volume 87, December 2022, 104255 Link
8. Machine learning-based prediction of harmful algal blooms in water supply reservoirs

Bongseok Jeong, Maria Renee Chapeta, Mingu Kim, Jinho Kim, Jihoon Shin, YoonKyung Cha

Water Quality Research Journal Volume 57, 4 Link
7. Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

TaeHo Kim, Jihoon Shin, DoYeon Lee, YoungWoo Kim, Eunhye Na, Jong-Hwan Park, Chaehong Lim, YoonKyung Cha

Water Research Volume 215, 15 May 2022, 118289 Link
6. Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Young Woo Kim, TaeHo Kim, Jihoon Shin, Dae-Seong Lee, Young-Seuk Park, Yeji Kim, YoonKyung Cha

Ecological Indicators Volume 137, April 2022, 108737 Link
5. Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

TaeHo Kim, DoYeon Lee, Jihoon Shin, YoungWoo Kim, YoonKyung Cha

Science of The Total Environment Volume 812, 15 March 2022, 152520 Link

2024

Multisite algal bloom predictions in a lake using graph attention networks

Nakgyeom Kim, Jihoon Shin, YoonKyung Cha

Environmental Engineering Research 2024; 29(2): 230210. Published online: June 13, 2023 Link
Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

Jihoon Shin, Gunhyeong Lee, Taeho Kim, Kyung Hwa Cho, Seok Min Hong, Do Hyuck Kwon, Jongcheol Pyo, Yoonkyung Cha

Science of The Total Environment Volume 912, 20 February 2024, 169540 Link
Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

Hyo Gyeom Kim, YoonKyung Cha, Kyung Hwa Cho

Water Research Volume 249, 1 February 2024, 120928 Link

2023

Changes in zooplankton community in response to a shift from lentic to lotic conditions in a regulated river

Taeseung Park, Gunhyeong Lee, Jihoon Shin, Jong-hwan Park, Byungwoong Choi, Dong-Kyun Kim, YoonKyung Cha

Ecological Informatics Volume 77, November 2023, 102236 Link
Data-driven models for predicting community changes in freshwater ecosystems: A review

Da-Yeong Lee, Dae-Seong Lee, YoonKyung Cha, Joong-Hyuk Min, Young-Seuk Park

Ecological Informatics Volume 77, November 2023, 102163 Link
Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

TaeHo Kim, Jihoon Shin, YoonKyung Cha

Journal of Cleaner Production Volume 414, 15 August 2023, 137564 Link
Hybrid model for daily streamflow and phosphorus load prediction

DoYeon Lee, Jihoon Shin, TaeHo Kim, Sangchul Lee, Dongho Kim, Yeonjeong Park, YoonKyung Cha

Water Sci Technol (2023) 88 (4): 975–990. Link
Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

Yongsung Kwon, YoonKyung Cha, Yeonjeong Park, Sangchul Lee

Scientific Reports volume 13, Article number: 9296 (2023) Link
학습 기반 최적화 방법론을 이용한 서울시 도로변대기측정망 확장

김태호, 신지훈, 김영우, 이도연, 백주은, 이도윤, 차윤경

Journal of Korean Society of Environmental Engineers 2023; 45(3): 148-160. Published online: March 31, 2023 Link
Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

Chang Keun Kang, Jihoon Shin, YoonKyung Cha, Min Sun Kim, Min Sun Choi, TaeHo Kim, Young-Kwon Park, Yong Jun Choi

Bioresource Technology Volume 369, 128455 Link

2022

Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Jihoon Shin, SangHyun Son, YoonKyung Cha

Sustainable Cities and Society Volume 87, December 2022, 104255 Link
Machine learning-based prediction of harmful algal blooms in water supply reservoirs

Bongseok Jeong, Maria Renee Chapeta, Mingu Kim, Jinho Kim, Jihoon Shin, YoonKyung Cha

Water Quality Research Journal Volume 57, 4 Link
Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

TaeHo Kim, Jihoon Shin, DoYeon Lee, YoungWoo Kim, Eunhye Na, Jong-Hwan Park, Chaehong Lim, YoonKyung Cha

Water Research Volume 215, 15 May 2022, 118289 Link
Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Young Woo Kim, TaeHo Kim, Jihoon Shin, Dae-Seong Lee, Young-Seuk Park, Yeji Kim, YoonKyung Cha

Ecological Indicators Volume 137, April 2022, 108737 Link
Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

TaeHo Kim, DoYeon Lee, Jihoon Shin, YoungWoo Kim, YoonKyung Cha

Science of The Total Environment Volume 812, 15 March 2022, 152520 Link

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Publications

Journals

19. Multisite algal bloom predictions in a lake using graph attention networks

18. Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

17. Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

16. Changes in zooplankton community in response to a shift from lentic to lotic conditions in a regulated river

15. Data-driven models for predicting community changes in freshwater ecosystems: A review

14. Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

13. Hybrid model for daily streamflow and phosphorus load prediction

12. Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

11. 학습 기반 최적화 방법론을 이용한 서울시 도로변대기측정망 확장

10. Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

9. Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

8. Machine learning-based prediction of harmful algal blooms in water supply reservoirs

7. Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

6. Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

5. Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

2024

Multisite algal bloom predictions in a lake using graph attention networks

Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

2023

Changes in zooplankton community in response to a shift from lentic to lotic conditions in a regulated river

Data-driven models for predicting community changes in freshwater ecosystems: A review

Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms

Hybrid model for daily streamflow and phosphorus load prediction

Assessing the impacts of dam/weir operation on streamflow predictions using LSTM across South Korea

학습 기반 최적화 방법론을 이용한 서울시 도로변대기측정망 확장

Machine learning-guided prediction of potential engineering targets for microbial production of lycopene

2022

Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Machine learning-based prediction of harmful algal blooms in water supply reservoirs

Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams

논문정보