Evidence map›Paper›PMID 34748103›Full record

ArticleEnvironmental monitoring and assessment2021

Decision tree for estimating groundwater contaminant through proxies considering seasonality and soil saturation.

Saha Dauji, Tirumalesh Keesari

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2021. 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

2 authors.

Saha DaujiNuclear Recycle Board, Bhabha Atomic Research Centre, Anushaktinagar, Mumbai, 400094, India. acad.dauji@gmail.com.ORCID http://orcid.org/0000-0001-5766-3567
Tirumalesh KeesariIsotope and Radiation Application Division, Bhabha Atomic Research Centre, Mumbai, 400085, India.ORCID http://orcid.org/0000-0002-6940-0403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chloride ion is an important indicator of water quality. Field measurement of chloride is difficult whereas laboratory measurement is both time-consuming and chemical intensive. The conservative nature of chloride and good correlation with electrical conductivity (EC) justifies its use as proxy for chloride estimations. Comparison of the best regression models (RMs) and data-driven decision tree (DT) model enables appreciation of relative merits of the two approaches for this purpose. Quantitative improvements over the models from literature are, increase in correlation (RM: 0.70 to 0.77; DT: 0.70 to 0.78) and decrease in relative errors (RM: MARE: 0.88 to 0.65 and RMSRE: 1.91 to 0.92; DT: MARE: 0.88 to 0.40; RMSRE: 1.91 to 0.54); thereby, DT has emerged as the better modeling approach for this case. Considering the influence of seasonality (pre-or post-monsoon) and degree of saturation of soil (water logged or water depleted) enabled the reduction of the correlation range (0.24-0.87) of the basic variables to a smaller range (0.44-0.89) for estimates of Cl

Indexed as

GroundwaterWater Pollutants, ChemicalDecision TreesEnvironmental MonitoringSoilSoilWater Pollutants, ChemicalChlorideData-driven estimationElectrical conductivityM5 algorithmWater qualityWater saturation

Identifiers

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Registered trials

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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.