TaeHo Kim - Achievements

Publications

Kim, Y., Kim, T., Shin, J., Lee, D., Park, Y., Kim, Y., Cha, Y. (2022). Validity evaluation of a machine learning model for chlorophyll-a retrieval from Sentinel-2 (MSI) in inland and coastal waters. Ecological Indicators [In press].
Kim, T., Shin, J., Na E., Lee, D., Park, J., Im C., & Cha, Y. (2021). Simultaneous feature engineering and interpretation for harmful algal bloom forecasts with a deep learning approach. Water Research [Moderate revision].
Kim, T., Lee, D., Shin, J., Kim, Y., & Cha, Y. (2022). Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams. Science of Total Environment, 152520. (https://doi.org/10.1016/j.scitotenv.2021.152520)
Cha, Y., Shin, J., Go, B., Lee, D. S., Kim, Y., Kim, T., & Park, Y. S. (2021). An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates. Journal of Environmental Management, 291, 112719. (https://doi.org/10.1016/j.jenvman.2021.112719)
Kim, Y., Kim, T., Shin, J., Go, B., Lee, M., Lee, J., Cho, K., & Cha, Y. (2021). Forecasting abrupt depletion of dissolved oxygen in urban streams using discontinuously measured hourly time‐series data. Water Resources Research, 57(4), e2020WR029188. (https://doi.org/10.1029/2020WR029188)
Shin, J., Yoon, S., Kim, Y., Kim, T., Go, B., & Cha, Y. (2021). Effects of class imbalance on resampling and ensemble learning for improved prediction of cyanobacteria blooms. Ecological Informatics, 61, 101202. (https://doi.org/10.1016/j.ecoinf.2020.101202)
Kim, T., Kim, Y., Shin, J., Go, B., & Cha, Y. (2020). Assessing land-cover effects on stream water quality in metropolitan areas using the water quality index. Water, 12(11), 3294. (Editor’s choice) (https://doi.org/10.3390/w12113294)
Lee, J., Ha, H., Lee, M., Kim, T., Cha, Y., & Koo, J. (2020). Assessment of water quality of major tributaries in Seoul using water quality index and cluster analysis. Journal of Korean Society of Environmental Engineers, 42(10), 452-462. (https://doi.org/10.4491/KSEE.2020.42.10.452)

Conferences

Kim, T., Lee, D., Shin, J., Kim, Y., & Cha, Y. Development of deep Bayesian networks for environment modeling. Korean Society of Environmental Engineers Winter Conference, 2021.
Lee, D., Kim, T., Shin, J., Kim, Y., & Cha, Y. Assessing real-time feature contribution change of environment variables in the occurrence of algal bloom warning. Korean Society of Environmental Engineers Winter Conference, 2021.
Kim, T., Shin, J., Kim, Y., Go, B., Beak, J., Lee, D., Lee., D., & Cha, Y. Establishment of optimization methodology for expanding roadside air quality monitoring network (AQMN) in Seoul. Korean Society of Environmental Engineers Winter Conference, 2020.
Go, B., Shin., J., Kim, Y., Kim, T., & Cha, Y. The effect of hyperparameter optimization method and generalization performance estimation method of machine learning model on the performance of river water quality forecasting model, Korean Society of Environmental Engineers Winter Conference, 2020.
Shin, J., Kim, Y., Kim, T., Go, B., Cha, Y., & Yoon, S. Improving the predicting performance of cyanobacteria bloom occurrence model using data resampling and ensemble learning. Korean Society of Environmental Engineers Winter Conference, 2020.
Kim, Y., Shin, J., Kim, T., Go, B., & Cha, Y. A study on the interpretable machine learning techniques of river dissolved oxygen forecasting model based on machine learning. Korean Society of Environmental Engineers Winter Conference, 2020.
Shin, J., Cha, Y., Kim, T., Lee, M., Kil, H., Ha, H., & Lee, J. Characterization of urban stream water quality using water quality index, Geophysical Research Abstracts, 2019.
Kim, T., Im, I., Youn, S., Jeon, H., Kim, Y., Shin, J., & Cha, Y. Identification of major water quality clusters using integrated water quality index in urban rivers. Korean Society of Environmental Engineers Winter Conference, 2018.

Patents

Lee, J., Lee, M., Lee, M., Lee, J., Kim, H., Cha, Y., & Kim, T., Extraction of major water quality management variables for rivers using factor analysis and the development of customized water quality index reflecting regional differences, Korea Patent [Patent Application].

Projects

Development of ecological prediction models and health assessment methods in lentic ecosystems, Korea Environmental Industry and Technology Institute (KEITI), Graduate Researcher, May 2020 – Present.
Development of the big data platform and standardization techniques for the management of waterworks and sewer systems, Environmental Industry and Technology Institute (KEITI), Graduate Researcher, Apr. 2020 – Present.
Development of a modularized AI-based model for the multiscale analysis of multimodal environmental data, Ministry of Science and ICT (MSIT), Graduate Researcher, Mar. 2020 – Present.
Developing optimization methodology of roadside air quality monitoring network (AQMN) for supporting fine dust management strategy in Seoul, Seoul Green Environmental Center (SGEC), Graduate Research Assistant, Mar. 2020 – Jan. 2021.
Development of prediction model for next generation water quality and aquatic ecosystem (‖) – Assessment of aquatic ecological models using water quality and aquatic ecosystem monitoring data, Undergraduate Research Assistant, Oct. 2018 – Oct. 2019.
Development of core element technology for Seoul-type river water quality management system based on big data to respond to climate change, Undergraduate Research Assistant, Oct. 2018 – Aug. 2019.