Evidence map›Paper›PMID 42411958›Full record

ArticleWater environment research : a research publication of the Water Environment Federation2026

Modeling of BOD and COD Concentrations in Aquaculture Sites of the Southeastern Coastal Region of Bangladesh Using Machine Learning.

Murchana Chakraborty, Mohammad Ismail, Jayanta Kumar Basak, Mohammad Mohinuzzaman, Bhola Paudel, Abdul Halim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Water environment research : a research publication of the Water Environment Federation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Murchana ChakrabortyDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, Bangladesh.
Mohammad IsmailDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, Bangladesh.
Jayanta Kumar BasakDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, Bangladesh.ORCID https://orcid.org/0000-0002-0718-1444
Mohammad MohinuzzamanDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, Bangladesh.
Bhola PaudelFuture Regions Research Centre, Ararat Jobs and Technology Precinct, Federation University, Mount Helen, Victoria, Australia.
Abdul HalimDepartment of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali, Bangladesh.

Funding

Research Cell, Noakhali Science and Technology University NSTU/RC-ESDM/T-24-25/100
6 · The paper itself

Abstract

Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) are key indicators of aquaculture water quality. However, few studies have explored the applicability of machine learning (ML) approaches for their prediction. Therefore, this study evaluated five ML models using key physicochemical water quality parameters, including water temperature (WT), electrical conductivity (EC), dissolved oxygen (DO), pH, total dissolved solids (TDS), turbidity (T), and water salinity (WS). The multilayer perceptron (MLP) outperformed the other models, improving R

Indexed as

AquacultureBiological Oxygen Demand AnalysisMachine LearningBangladeshEnvironmental MonitoringMultilayer PerceptronsPrediction AlgorithmsPredictive Learning ModelsWater Qualityaquaculture watersBOD and COD prediction modelmachine learning algorithmswater quality parameters

Identifiers

PMID42411958
PMCPMC13349447

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.