Current status and future of optimization technologies for water treatment and water environment management using reinforcement learning View count 233

Jungsu Park

Journal of the Korean Society of Water and Wastewater :: Vol.39 No.5 pp.291-304

DOI:https://doi.org/10.11001/jksww.2025.39.5.291

Analysis of cumulative damage in water distribution system using machine learning based corrosion depth prediction models View count 186

Hyuk Jae Kwon

Journal of the Korean Society of Water and Wastewater :: Vol.39 No.5 pp.313-324

DOI:https://doi.org/10.11001/jksww.2025.39.5.313

Bayesian optimization with blocked time series cross validation for wastewater quality prediction View count 119

Hyeongju Park, Kyudae Shim, Young Mo Kim, Sangchul Lee

Journal of the Korean Society of Water and Wastewater :: Vol.39 No.4 pp.199-211

DOI:https://doi.org/10.11001/jksww.2025.39.4.199

An early fouling alarm method for a ceramic microfiltration pilot plant using machine learning View count 475

Dohyun Tak, Dongkeon Kim, Jongmin Jeon, Suhan Kim

Journal of the Korean Society of Water and Wastewater :: Vol.37 No.5 pp.271-279

DOI:https://doi.org/10.11001/jksww.2023.37.5.271

Methodology for leakage diagnosis of gate valve using machine learning and flow rate prediction using pressure difference View count 406

Sumin Lee, Kwangjun Jung, Hyunjun Kim

Journal of the Korean Society of Water and Wastewater :: Vol.37 No.3 pp.119-126

DOI:https://doi.org/10.11001/jksww.2023.37.3.119

Development of a model to predict water quality using an automated machine learning algorithm View count 252

Jungsu Park

Journal of the Korean Society of Water and Wastewater :: Vol.36 No.6 pp.329-337

DOI:https://doi.org/10.11001/jksww.2022.36.6.329

Development of ensemble machine learning model considering the characteristics of input variables and the interpretation of model performance using explainable artificial intelligence View count 309

Jungsu Park

Journal of the Korean Society of Water and Wastewater :: Vol.36 No.4 pp.239-248

DOI:https://doi.org/10.11001/jksww.2022.36.4.239

Application of machine learning in water industry: A review View count 1021

Wonki O, Haenam Jang, Seung Gu Shin

Journal of the Korean Society of Water and Wastewater :: Vol.36 No.1 pp.9-21

DOI:https://doi.org/10.11001/jksww.2022.36.1.9

Effect of input variable characteristics on the performance of an ensemble machine learning model for algal bloom prediction View count 623

Byeong-Koo Kang, Jungsu Park

Journal of the Korean Society of Water and Wastewater :: Vol.35 No.6 pp.417-424

DOI:https://doi.org/10.11001/jksww.2021.35.6.417

A study on applying random forest and gradient boosting algorithm for Chl-a prediction of Daecheong lake View count 561

Sang-Min Lee, Il-Kyu Kim

Journal of the Korean Society of Water and Wastewater :: Vol.35 No.6 pp.507-516

DOI:https://doi.org/10.11001/jksww.2021.35.6.507

Comparative characteristic of ensemble machine learning and deep learning models for turbidity prediction in a river View count 807

Jungsu Park

Journal of the Korean Society of Water and Wastewater :: Vol.35 No.1 pp.83-91

DOI:https://doi.org/10.11001/jksww.2021.35.1.083

Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong River (focusing on water quality and quantity factors) View count 1194

Sang-Min Lee, Kyeong-Deok Park, Il-Kyu Kim

Journal of the Korean Society of Water and Wastewater :: Vol.34 No.4 pp.277-288

DOI:https://doi.org/10.11001/jksww.2020.34.4.277

Prediction of high turbidity in rivers using LSTM algorithm View count 2337

Jungsu Park, Hyunho Lee

Journal of the Korean Society of Water and Wastewater :: Vol.34 No.1 pp.35-43

DOI:https://doi.org/10.11001/jksww.2020.34.1.035