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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
doi: https://doi.org/10.4491/eer.2023.210
Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning
Do Hyuck Kwon, Seok Min Hong, Ather Abbas, JongCheol Pyo, Hyung-Kun Lee, Sang-Soo Baek, Kyung Hwa Cho
Environmental Engineering Research 2023;28(1):210280  Published online January 26, 2022
doi: https://doi.org/10.4491/eer.2021.280
Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine
Hye-Suk Yi, Bomi Lee, Sangyoung Park, Keun-Chang Kwak, Kwang-Guk An
Environmental Engineering Research 2019;24(3):404-411.   Published online October 5, 2018
doi: https://doi.org/10.4491/eer.2018.245
A novel method for cell counting of Microcystis colonies in water resources using a digital imaging flow cytometer and microscope
Jungsu Park, Yongje Kim, Minjae Kim, Woo Hyoung Lee
Environmental Engineering Research 2019;24(3):397-403.   Published online October 4, 2018
doi: https://doi.org/10.4491/eer.2018.266
Application of hybrid material, modified sericite and pine needle extract, for blue-green algae removal in the lake
Hee-Jeong Choi
Environmental Engineering Research 2018;23(4):364-373.   Published online March 28, 2018
doi: https://doi.org/10.4491/eer.2017.207
Comparative assessment on the influences of effluents from conventional activated sludge and biological nutrient removal processes on algal bloom in receiving waters
Chul Park, Diane Sheppard, Dongke Yu, Sona Dolan, Heonseop Eom, Jane Brooks, Douglas Borgatti
Environmental Engineering Research 2016;21(3):276-283.   Published online May 3, 2016
doi: https://doi.org/10.4491/eer.2015.144
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