Current status and future of optimization technologies for water treatment and water environment management using reinforcement learning View count 242

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 196

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 121

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

Feature extraction techniques based on fourier transform and MFCC for detecting variable leak sounds View count 117

Hyunjun Kim, Kwangjun Jung

Journal of the Korean Society of Water and Wastewater :: Vol.39 No.1 pp.25-34

DOI:https://doi.org/10.11001/jksww.2025.39.1.25

Development of a model to predict water quality using an automated machine learning algorithm View count 253

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 310

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 1028

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 627

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 562

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

Development of benthic macroinvertebrate species distribution models using the Bayesian optimization View count 1014

ByeongGeon Go, Jihoon Shin, Yoonkyung Cha

Journal of the Korean Society of Water and Wastewater :: Vol.35 No.4 pp.259-275

DOI:https://doi.org/10.11001/jksww.2021.35.4.259

Comparative characteristic of ensemble machine learning and deep learning models for turbidity prediction in a river View count 808

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 1196

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 2340

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