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Education

2018-2022: PhD student at Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, South Korea
2013-2017: M. S. Hydrogeology and Environmental Science, Goettingen University, Germany
2008-2013: B. S. Applied Geology, University of the Punjab, Pakistan

Thesis titles:

Ph.D. thesis: Artificial intelligence for modeling of surface water resources: Application from streamflow to water quality
Master’s thesis: Estimation of hydraulic aquifer parameters by integral transform methods applied to aquifer and river stream head responses (Leine River)
Bachelor’s thesis: Hydrogeological investigations of Munara area(Pakistan) and surroundings

Experience

Postdoc Researcher at KAUST from Sept 2023 -
- data-driven modeling of hydrological extreme events
Postdoc Researcher at Weil lab from Sept 2022 - Aug 2023
- data driven modeling of water quality
- data driven modeling of wastewater treatment
Researcher at WEIL lab from 2017 - 2018
- Watershed modeling with HSPF for Ulsan city Korea
- Coupling of HSPF model with analytical groundwater models
Internee at G.E.O.S GmbH Freiberg from 2016 - 2017
- Development of thermo-mechanical models of magma emplacement, subduction and convection in MATLAB
- Hydrological mapping of Saxony with Surpac and ArcGIS
- Modeling of reactive groundwater transport using PhreeqC and COMSOL
- Modeling Hydraulic fracturing using KGD, PKN and P3D models with MATLAB
- numerical modeling of groundwater flow, temperature distribution in crust and convection cell studies using MATLAB
- Development of coupled heat transport model in geothermal wells and neighboring rock
Jr. GIS professional at The Urban Unit from Jun. 2013 - Sep. 2013
- Preparation of Landuse maps

Skills

Machine Learning
- AI4Water
- scikit-learn, keras, Tensorflow, PyTorch
Modeling
- Surface Water (HSPF, SWAT)
- Groundwater (MODFlow)

Publications

Surface and sub-surface flow estimation at high temporal resolution using deep neural networks

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.

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

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.

Developing a deep learning model for the simulation of micro-pollutants in a watershed

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.

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

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.

Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant

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.

In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models

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.

Deep learning for pH prediction in water desalination using membrane capacitive deionization

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.

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

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).

AI4Water v1. 0: An open-source python package for modeling hydrological time series using data-driven methods

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.

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

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,

An open-source deep learning model for predicting effluent concentration in capacitive deionization

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.

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

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

Developing a data-driven modeling framework for simulating a chemical accident in freshwater

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.

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

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.

Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms

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.

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

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

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

Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

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.

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

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.

Ather Abbas

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