Evidence map›Paper›PMID 39027495›Full record

ArticleHeliyon2024

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 and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Development and deployment of a groundwater quality prediction tool for Béchar, Southwest Algeria: benchmarking seven machine learning algorithms.Water science and technology : a journal of the International Association on Water Pollution Research · 2026
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Md Galal UddinSchool of Engineering, University of Galway, Ireland.
M M Shah Porun RanaThe Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh.
Mir Talas Mahammad DigantaSchool of Engineering, University of Galway, Ireland.
Apoorva BamalSchool of Engineering, University of Galway, Ireland.
Abdul Majed SajibSchool of Engineering, University of Galway, Ireland.
Mohamed AbiouiGeosciences, Environment and Geomatics Laboratory (GEG), Department of Earth Sciences, Faculty of Sciences, Ibnou Zohr University, Agadir, Morocco.
Molla Rahman ShaiburLaboratory 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 AshekuzzamanDepartment of Civil, Structural and Environmental Engineering, and Sustainable Infrastructure Research & Innovation Group, Munster Technological University, Cork, Ireland.
Mohammad Reza NikooDepartment of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
Azizur RahmanSchool of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, Australia.
Md MoniruzzamanThe Department of Geography and Environment, Jagannath University, Dhaka, Bangladesh.
Agnieszka I OlbertSchool of Engineering, University of Galway, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

GroundwaterMachine learningRMS-WQI modelUncertaintyWater quality index (WQI)

Identifiers

PMID39027495
PMCPMC11255574

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.