Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach
Md Galal Uddin,
M.M. Shah Porun Rana,
Mir Talas Mahammad Diganta,
Apoorva Bamal,
Abdul Majed Sajib,
Mohamed Abioui,
Molla Rahman Shaibur,
S.M. Ashekuzzaman,
Mohammad Reza Nikoo,
Azizur Rahman,
Md Moniruzzaman,
Agnieszka I. Olbert
Affiliations
Md Galal Uddin
School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland; Corresponding author. Civil Engineering, College of Science and Engineering, University of Galway, Ireland.
M.M. Shah Porun Rana
The Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh
Mir Talas Mahammad Diganta
School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
Apoorva Bamal
School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
Abdul Majed Sajib
School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
Mohamed Abioui
Geosciences, Environment and Geomatics Laboratory (GEG), Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir, Morocco; MARE-Marine and Environmental Sciences Centre-Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal; Laboratory for Sustainable Innovation and Applied Research, Universiapolis—International University of Agadir, Agadir, Morocco
Molla Rahman Shaibur
Laboratory of Environmental Chemistry, Department of Environmental Science and Technology, Faculty of Applied Science and Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
S.M. Ashekuzzaman
Department of Civil, Structural and Environmental Engineering, and Sustainable Infrastructure Research & Innovation Group, Munster Technological University, Cork, Ireland
Mohammad Reza Nikoo
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman
Azizur Rahman
School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, Australia; The Gulbali Institute of Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, Australia
Md Moniruzzaman
The Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh
Agnieszka I. Olbert
School of Engineering, University of Galway, Ireland; Ryan Institute, University of Galway, Ireland; MaREI Research Centre, University of Galway, Ireland; Eco-HydroInformatics Research Group (EHIRG), Civil Engineering, University of Galway, Ireland
Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3−), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as “fair.” In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (<1 %) in predicting WQI scores. These findings implied the efficacy of the RMS-WQI model in accurately assessing GWQ in coastal areas, that would ultimately assist regional environmental managers and strategic planners for effective monitoring and sustainable management of coastal GW resources.
