Evidence map›Paper›PMID 37159726›Full record

ArticleJournal of environmental health science & engineering2023

Prediction models for groundwater quality parameters using a multiple linear regression (MLR): a case study of Kermanshah, Iran.

Parisa Dargahi, Simin Nasseri, Mahdi Hadi, Ramin Nabizadeh Nodehi, Amir Hossein Mahvi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of environmental health science & engineering, 2023. 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. Ensemble machine learning method for δScientific reports · 2026
    Article
  2. Article
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

5 authors.

Parisa DargahiDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Simin NasseriDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Mahdi HadiCenter for Water Quality Research (CWQR), Institute for Environmental Research(IER), Tehran University of Medical Sciences, Tehran, Iran.
Ramin Nabizadeh NodehiDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Amir Hossein MahviDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Groundwater is one of the major sources of exploitation in arid and semiarid regions. Spatial and temporal quality distribution is an important factor in groundwater management. Thus for protecting groundwater quality, data production on spatial and temporal distribution is essential. The present study has applied multiple linear regression (MLR) techniques to predict the fitness of groundwater quality in Kermanshah province, west of Iran. The parameters examined were Total dissolved solids (TDS), Total hardness (TH), Sodium adsorption ratio (SAR). the quality variables were modelled by MLR. Finally, the performance of the models was assessed using the coefficient of determination (R

Indexed as

Correlation coefficientsGroundwater qualityModelingMultiple linear regressions (MLR)

Identifiers

PMID37159726
PMCPMC10163177

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.