Ather Abbas

Ather Abbas

PhD in Artificial intelligence in surface water resources

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

What is scikit-learn?

less than 1 minute read

Published:

Scikit-learn has been the lingua-franca of machine learning community for over a decade now. Developed in early 2000’s, the paper about scikit-learn came in 2011 and 2013. The paper is well written, and describes the basic principles of the library. The very fact that the paper of a machine learning library is still very much relevant today, speaks volumes about the strong principles and robust software design of scikit-learn library. No doubt, the library has influenced many onward machine learning libraries and almost all the ‘mainstream’ machine learning libraries have borrowed many concepts from it. Since the success of scikit-learn, there has been plethora of scikits, sickit-optimize for optimization of hyperparameters, scikit-image for image processing, to name a few. This post has been influenced by the original paper and my experience of using scikit-learn. The purpose is to provide an overview of scikit-learn with code examples.

publications

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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