:: Journal of the Korean Society of Water and Wastewater ::

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Application of machine learning in water industry: A review

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
Open abstract
Effect of input variable characteristics on the performance of an ensemble machine learning model for algal bloom prediction

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
Open abstract
A study on applying random forest and gradient boosting algorithm for Chl-a prediction of Daecheong lake

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
Open abstract
Development of benthic macroinvertebrate species distribution models using the Bayesian optimization

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
Open abstract
Comparative characteristic of ensemble machine learning and deep learning models for turbidity prediction in a river

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

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
Open abstract
Prediction of high turbidity in rivers using LSTM algorithm

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
Open abstract