Published in Water Resources Research, 2025
Recommended citation: Yang, Y., Feng, D., Beck, H. E., Hu, W., Abbas, A., Sengupta, A., ... & Pan, M. (2025). Global daily discharge estimation based on grid long shortâterm memory (LSTM) model and river routing. Water Resources Research, 61(6), e2024WR039764. https://doi.org/10.1029/2024WR039764
Published in HESS, 2025
Recommended citation: Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., ... & Beck, H. E. (2025). Comprehensive global assessment of 23 gridded precipitation datasets across 16,295 catchments using hydrological modeling. EGUsphere, 2025, 1-31. https://doi.org/10.5194/egusphere-2024-4194
Published in Journal of Open Source Software, 2024
Recommended citation: Rubab, F., Iftikhar, S., & Abbas, A. (2024). SeqMetrics: a unified library for performance metrics calculation in Python. Journal of Open Source Software, 9(99), 6450. https://doi.org/10.21105/joss.06450
Published in Water Research X, 2023
Recommended citation: Kim, C. M., Jaffari, Z. H., Abbas, A., Chowdhury, M. F., & Cho, K. H. (2023). Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. Journal of Hazardous Materials, 132995. https://doi.org/10.1016/j.jhazmat.2023.132995
Published in Journal of Hazardous Materials, 2023
Recommended citation: Kim, C. M., Jaffari, Z. H., Abbas, A., Chowdhury, M. F., & Cho, K. H. (2023). Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. Journal of Hazardous Materials, 132995. https://doi.org/10.1016/j.jhazmat.2023.132995
Published in Journal of Hydrology, 2023
Recommended citation: Abbas, A., Park, M., Baek, S. S., & Cho, K. H. (2023). Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams. Journal of Hydrology, 626, 130240. https://doi.org/10.1016/j.jhydrol.2023.130240
Published in Journal of Hazardous materials, 2023
Recommended citation: https://doi.org/10.1016/j.jhazmat.2023.132773
Published in GIS and Remote Sensing, 2023
Recommended citation: https://www.tandfonline.com/doi/pdf/10.1080/15481603.2023.2249753
Published in Environmental Modeling and Software, 2023
Recommended citation: Kwon, D. H., Hong, S. M., Abbas, A., Pyo, J., Lee, H. K., Baek, S. S., & Cho, K. H. (2023). Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environmental Engineering Research, 28(1). https://doi.org/10.1016/j.envsoft.2023.105805
Published in Ecological Informatics, 2023
Recommended citation: Jang, J., Abbas, A., Kim, H., Rhee, C., Shin, S. G., Chun, J. A., ... & Cho, K. H. (2023). Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms. Ecological Informatics, 102370. https://authors.elsevier.com/c/1i5qV5c6cL2WXM
Published in Separation and Purification Technology, 2023
Recommended citation: Iftikhar, S., Zahra, N., Rubab, F., Sumra, R. A., Khan, M. B., Abbas, A., & Jaffari, Z. H. (2023). Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials. Separation and Purification Technology, 326, 124891. https://doi.org/10.1016/j.seppur.2023.124891
Published in Journal of Cleaner Production, 2023
Recommended citation: Kim, S., Abbas, A., Pyo, J., Kim, H., Hong, S. M., Baek, S. S., & Cho, K. H. (2023). Developing a data-driven modeling framework for simulating a chemical accident in freshwater. Journal of Cleaner Production, 425, 138842. https://doi.org/10.1016/j.jclepro.2023.138842
Published in Journal of Materials Chemistry A, 2023
Recommended citation: Jaffari, Z. H., Abbas, A., Umar, M., Kim, E-S., & Cho, K. H. (2023). Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions https://doi.org/10.1039/D3TA00019B
Published in Science of the Total Environment, 2023
Recommended citation: Son, M., Yoon, N., Park, S., Abbas, A., & Cho, K. H. (2023). An open-source deep learning model for predicting effluent concentration in capacitive deionization. Science of The Total Environment, 856, 159158. https://doi.org/10.1016/j.scitotenv.2022.159158
Published in Journal of Hazardous Materials, 2022
Recommended citation: Jaffari, Z. H., Abbas, A., Lam, S-M., Sanghun, P., Chon, K., Kim, E-S., & Cho, K. H. (2022). Machine learning approaches to predict the photocatalytic performance of bismuch ferrite-based materials in the removal of malachite green. Journal of Hazardous Materials, https://doi.org/10.1016/j.jhazmat.2022.130031
Published in Geoscientific Model Development, 2022
Recommended citation: Abbas, A., Boithias, L., Pachepsky, Y., Kim, K., Chun, J. A., & Cho, K. H. (2022). AI4Water v1. 0: an open-source python package for modeling hydrological time series using data-driven methods. Geoscientific Model Development, 15(7), 3021-3039. https://doi.org/10.5194/gmd-15-3021-2022
Published in Environmental Engineering Research, 2022
Recommended citation: Kwon, D. H., Hong, S. M., Abbas, A., Pyo, J., Lee, H. K., Baek, S. S., & Cho, K. H. (2023). Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environmental Engineering Research, 28(1). https://doi.org/10.4491/eer.2021.280
Published in Desalination, 2021
Recommended citation: Son, M., Yoon, N., Jeong, K., Abass, A., Logan, B. E., & Cho, K. H. (2021). Deep learning for pH prediction in water desalination using membrane capacitive deionization. Desalination, 516, 115233. https://doi.org/10.1016/j.desal.2021.115233
Published in Hydrology and Earth System Sciences, 2021
Recommended citation: Abbas, A., Baek, S., Silvera, N., Soulileuth, B., Pachepsky, Y., Ribolzi, O., ... & Cho, K. H. (2021). In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models. Hydrology and Earth System Sciences, 25(12), 6185-6202. https://doi.org/10.5194/hess-25-6185-2021
Published in Desalination, 2021
Recommended citation: Yoon, N., Kim, J., Lim, J. L., Abbas, A., Jeong, K., & Cho, K. H. (2021). Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant. Desalination, 512, 115107. https://doi.org/10.1016/j.desal.2021.115107
Published in Water Research, 2021
Recommended citation: Jang, J., Abbas, A., Kim, M., Shin, J., Kim, Y. M., & Cho, K. H. (2021). Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models. Water Research, 196, 117001. https://doi.org/10.1016/j.watres.2021.117001
Published in Journal of Cleaner Production, 2021
Recommended citation: Yun, D., Abbas, A., Jeon, J., Ligaray, M., Baek, S. S., & Cho, K. H. (2021). Developing a deep learning model for the simulation of micro-pollutants in a watershed. Journal of Cleaner Production, 300, 126858. https://doi.org/10.1016/j.jclepro.2021.126858
Published in Water Research, 2021
This paper is prediction of antibiotic resistance genes at a recreational beach in South Korea using deep learning..
Recommended citation: Jeong, K., Abbas, A., Shin, J., Son, M., Kim, Y. M., & Cho, K. H. (2021). Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models. Water Research, 205, 117697. https://doi.org/10.1016/j.watres.2021.117697
Published in Journal of Hydrology, 2020
Recommended citation: Abbas, A., Baek, S., Kim, M., Ligaray, M., Ribolzi, O., Silvera, N., ... & Cho, K. H. (2020). Surface and sub-surface flow estimation at high temporal resolution using deep neural networks. Journal of Hydrology, 590, 125370. https://doi.org/10.1016/j.jhydrol.2020.125370