Article

  • Sajib, A. M., Diganta, M. T. M., Moniruzzaman, M., Rahman, A., Dabrowski, T., Uddin, M. G., & Olbert, A. I. (2024). Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches. Ecological Informatics, 102514. https://doi.org/10.1016/j.ecoinf.2024.102514
  • Uddin, M. G., Imran, M. H., Sajib, A. M., Hasan, M. A., Diganta, M. T. M., Dabrowski, T., ... & Moniruzzaman, M. (2024). Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches. Journal of Contaminant Hydrology, 104307. https://doi.org/10.1016/j.jconhyd.2024.104307
  • Uddin, M. G., Nash, S., Rahman, A., Dabrowski, T., & Olbert, A. I. (2023). Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches. Environmental Research, 117755. https://doi.org/10.1016/j.envres.2023.117755
  • Sajib, A. M., Diganta, M. T. M., Rahman, A., Dabrowski, T., Olbert, A. I., & Uddin, M. G. (2023). Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach. Groundwater for Sustainable Development, 101049. https://doi.org/10.1016/j.gsd.2023.101049
  • Uddin, M. G., Diganta, M. T. M., Sajib, A. M., Rahman, A., Nash, S., Dabrowski, T., Ahmadian, R., Hartnett, M., & Olbert, A. I. (2023). Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model. Environmental Pollution, 336(May), 122456. https://doi.org/10.1016/j.envpol.2023.122456 
  • Uddin, M. G., Diganta, M. T. M., Sajib, A. M., Hasan, M. A., Moniruzzaman, M., Rahman, A., Olbert, A. I., & Moniruzzaman, M. (2023). Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches. Heliyon, 9(9), e19668. https://doi.org/10.1016/j.heliyon.2023.e19668
  • Uddin, M. G., Jackson, A., Nash, S., Rahman, A., & Olbert, A. I. 2023. Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland. Science of The Total Environment, 165960. https://doi.org/10.1016/j.scitotenv.2023.165960

     

  • Uddin, M. G., Rahman, A., Nash, S., Talas, M., Diganta, M., Sajib, A. M., Moniruzzaman, M., & Olbert, A. I. (2023). Marine waters assessment using improved water quality model incorporating machine learning approaches. Journal of Environmental Management, 344(May), 118368. https://doi.org/10.1016/j.jenvman.2023.118368

  • Olbert, A. I., Moradian, S., Nash, S., Comer, J., Kazmierczak, B., Falconer, R., & Hartnett, M. 2023. Combined statistical and hydrodynamic modelling of compound flooding in coastal areas-Methodology and application. Journal of Hydrology, 129383. https://doi.org/10.1016/j.jhydrol.2023.129383

  • Moradian, S., Olbert, A. I., Gharbia, S., & Iglesias, G. 2023. Copula-based projections of wind power: Ireland as a case study. Renewable and Sustainable Energy Reviews, 175, 113147. https://doi.org/10.1016/j.rser.2023.113147

  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2023. A sophisticated model for rating water quality. Science of The Total Environment 869, 161614. https://doi.org/10.1016/j.scitotenv.2023.161614

  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2023. Assessing optimization techniques for improving water quality model. J Clean Prod 385, 135671. https://doi.org/10.1016/j.jclepro.2022.135671

  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2023. Performance analysis of the water quality index model for predicting water state using machine learning techniques. Process Safety and Environmental Protection 169, 808–828. https://doi.org/10.1016/j.psep.2022.11.073

  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2023. A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Res 229, 119422. https://doi.org/10.1016/j.watres.2022.119422

  • Uddin, M.G., Nash, S., Talas, M., Diganta, M., Rahman, A., Olbert, A.I., 2022. Robust machine learning algorithms for predicting coastal water quality index, Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2022.115923

  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2022. A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Res 118532. https://doi.org/10.1016/J.WATRES.2022.118532

  • Parween, S., Siddique, N. A., Diganta, M. T. M., Olbert, A. I., & Uddin, M. G. (2022). Assessment of urban river water quality using modified NSF water quality index model at Siliguri city, West Bengal, India. Environmental and Sustainability Indicators, 16, 100202. https://doi.org/10.1016/j.indic.2022.100202

  • Uddin, M.G., Nash, S., Olbert, A.I., 2021. A review of water quality index models and their use for assessing surface water quality. Ecol Indic 122, 107218. https://doi.org/10.1016/j.ecolind.2020.107218

Book Chapter

  • Uddin, M.G., Nash, S., Diganta, M.T.M., Rahman, A., Olbert, A.I., 2022c. A comparison of geocomputational models for validating geospatial distribution of water quality index, in: Priyanka, H., Rahman, A., Basant agarwal, Binita Tiwari (Eds.), Computational Statistical Methodologies and Modeling for Artificial Intelligence. CRC Press, Taylor & Francis Publisher, USA. http://dx.doi.org/10.1201/9781003253051-16

 Conference Paper

  • Olbert, A. I., Moradian, S., and Uddin, M. G. 2023. Machine learning modelling of compound flood events, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13083, https://doi.org/10.5194/egusphere-egu23-13083
  • Diganta, M. T. M., Uddin, M. G., and Olbert, A. I. 2023. Assessing the atmospheric correction algorithms for improving the retrieval data accuracy in the remote sensing technique, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13477, https://doi.org/10.5194/egusphere-egu23-13477
  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2022. Development of an efficient water quality model using cutting-edge artificial intelligence techniques. Data science in regional policy: housing and workforce dynamics at Charles Sturt University, Wagga Wagga, Australia. Australia and New Zealand Regional Science Association International 45th Annual Conference. pp-19
  • Diganta, M. T. M., Uddin, M. G., and Olbert, A. I. 2022. Assessment of algorithms for atmospheric correction in the remote sensing technique to retrieve chlorophyll-a more precisely. Data science in regional policy: housing and workforce dynamics at Charles Sturt University, Wagga Wagga, Australia. Australia and New Zealand Regional Science Association International 45th Annual Conference. pp-20.
  • Sajib, A. M., Uddin, M. G., and Olbert, A. I. 2022. Improving water quality monitoring program using cutting-edge artificial intelligence-machine learning-remote sensing techniques. Data science in regional policy: housing and workforce dynamics at Charles Sturt University, Wagga Wagga, Australia. Australia and New Zealand Regional Science Association International 45th Annual Conference. pp-21.
  • Uddin, G., Nash, S., Olbert, Agnieszka Indiana, 2022. Optimization of parameters in a water quality index model using principal component analysis, in: Proceedings of the 39th IAHR World Congress. International Association for Hydro-Environment Engineering and Research (IAHR), Spain, pp. 5739–5744. https://doi.org/10.3850/IAHR-39WC2521711920221326
  • Uddin, M.G., Nash, S., Rahman, A., Olbert, A.I., 2022. Development of a water quality index model - a comparative analysis of various weighting methods, in: Çiner, Prof.Dr.A. (Ed.), Mediterranean Geosciences Union Annual Meeting (MedGU-21). Spinger, Istanbul, pp. 1–6.
  • Uddin, M.G., Stephen Nash, Olbert, A.I., 2020. Application of Water Quality Index Models to an Irish Estuary, in: Civil and Environmental Research. Civil Engineering Research Association of Ireland (CERAI), pp. 576–581.
  • Uddin, M.G., Olbert, A.I., Nash, S., 2020. Assessment of water quality using Water Quality Index (WQI) models and advanced geostatistical technique, in: Civil Engineering Research Association of Ireland (CERAI). Civil Engineering Research Association of Ireland (CERAI), Cork Institute of Technology, Cork, pp. 594–599.