Ather Abbas

Ather Abbas

PhD in Artificial intelligence in surface water resources

Publications

37. Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18,428 Catchments Using Hydrological Modeling

Published in Hydrology and Earth System Sciences, 2026

Recommended citation: Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Tan, J., & Beck, H. E. (2026). Comprehensive global assessment of 24 gridded precipitation datasets across 18 428 catchments using hydrological modeling. Hydrology and Earth System Sciences, 30(11), 3399-3423. https://doi.org/10.5194/hess-30-3399-2026

28. Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models

Published in Water Research, 2024

Recommended citation: Jeong, H., Abbas, A., Kim, H. G., Van Hoan, H., Van Tuan, P., Long, P. T., Lee, E., & Cho, K. H. (2024). Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models. Water Research, 266, 122404. https://doi.org/10.1016/j.watres.2024.122404

26. Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater

Published in Journal of Hazardous Materials, 2024

Recommended citation: Jaffari, Z. H., Na, S., Abbas, A., Park, K. Y., & Cho, K. H. (2024). Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater. Journal of Hazardous Materials, 468, 133762. https://doi.org/10.1016/j.jhazmat.2024.133762

25. Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision

Published in Journal of Environmental Chemical Engineering, 2024

Recommended citation: Ishtiaq, R., Zahra, N., Iftikhar, S., Rubab, F., Sultan, K., Abbas, A., Lam, S.-M., Jaffari, Z. H., & Park, K. Y. (2024). Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision. Journal of Environmental Chemical Engineering, 12(2), 112238. https://doi.org/10.1016/j.jece.2024.112238

24. Simulation models of microbial community, pH, and volatile fatty acids of anaerobic digestion developed by machine learning

Published in Journal of Water Process Engineering, 2024

Recommended citation: Yu, S. I., Jeong, H., Shin, J., Shin, S. G., Abbas, A., Yun, D., Bae, H., & Cho, K. H. (2024). Simulation models of microbial community, pH, and volatile fatty acids of anaerobic digestion developed by machine learning. Journal of Water Process Engineering, 60, 105225. https://doi.org/10.1016/j.jwpe.2024.105225

23. Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal

Published in Journal of Hazardous Materials, 2024

Recommended citation: Kim, C. M., Jaffari, Z. H., Abbas, A., Chowdhury, M. F., & Cho, K. H. (2024). 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, 465, 132995. https://doi.org/10.1016/j.jhazmat.2023.132995

22. Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents

Published in Journal of Hazardous Materials, 2024

Recommended citation: Jaffari, Z. H., Abbas, A., Kim, C.-M., Shin, J., Kwak, J., Son, C., Lee, Y.-G., Kim, S., Chon, K., & Cho, K. H. (2024). Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents. Journal of Hazardous Materials, 462, 132773. https://doi.org/10.1016/j.jhazmat.2023.132773

20. Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials

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

16. Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Published in Environmental Modelling & Software, 2023

Recommended citation: Hong, S. M., Abbas, A., Kim, S., Kwon, D. H., Yoon, N., Yun, D., Lee, S., Pachepsky, Y., Pyo, J., & Cho, K. H. (2023). Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system. Environmental Modelling & Software, 168, 105805. https://doi.org/10.1016/j.envsoft.2023.105805

14. Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions

Published in Journal of Materials Chemistry A, 2023

Recommended citation: Jaffari, Z. H., Abbas, A., Umer, 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. Journal of Materials Chemistry A, 11(16), 9009-9018. https://doi.org/10.1039/D3TA00019B

12. Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green

Published in Journal of Hazardous Materials, 2023

Recommended citation: Jaffari, Z. H., Abbas, A., Lam, S.-M., Park, S., Chon, K., Kim, E.-S., & Cho, K. H. (2023). Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green. Journal of Hazardous Materials, 442, 130031. https://doi.org/10.1016/j.jhazmat.2022.130031

9. Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts

Published in Journal of Materials Chemistry A, 2022

Recommended citation: Umer, M., Umer, S., Zafari, M., Ha, M., Anand, R., Hajibabaei, A., Abbas, A., Lee, G., & Kim, K. S. (2022). Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts. Journal of Materials Chemistry A, 10(12), 6679-6689. https://doi.org/10.1039/D1TA09878K

7. In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

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

5. Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Published in Water Research, 2021

Prediction of biogas production in anaerobic co-digestion of organic wastes using a dual-stage attention deep learning model.

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