article · Heliyon
Coastal groundwater monitoring is critical for sustaining human health, agriculture, and natural ecosystems, but traditional water quality index models frequently suffer from inconsistent results. To address these limitations, a data-driven approach combining a root mean squared water quality index model with the extreme gradient boosting machine learning algorithm was evaluated in the coastal Bhola district of Bangladesh. The assessment tracked eleven chemical and physical indicators, identifying elevated concentrations of potassium, calcium, and magnesium that exceeded guideline limits. Overall water quality scores across all sampled sites averaged 65.2, falling into a fair rating category. The extreme gradient boosting model demonstrated strong predictive capability with an R-squared of 0.97, while the combined index model achieved less than one percent uncertainty, providing an effective framework for reliable coastal groundwater assessment.
Coastal communities depend on groundwater for basic survival, farming, and ecosystem health. Standard water quality metrics often give conflicting results that complicate environmental management. Demonstrating that machine learning can reliably process multi-parameter water data helps regional authorities make dependable decisions about contamination risks and the long-term sustainability of vital coastal water supplies.
This research provides an applied and tested computational approach that could inform software tools for water quality monitoring. Potential end users include regional environmental management agencies, strategic water planners, and municipal authorities. Because the system has only been tested on regional field data from one coastal district, further development, multi-site validation, and software packaging are necessary before it can be deployed as an operational digital monitoring tool.
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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 (NO<sub>3</sub> <sup>-</sup>), ammonium (NH<sub>4</sub> <sup>+</sup>), 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 (R<sup>2</sup> = 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.
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DOI: 10.1016/j.heliyon.2024.e33082
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