Evidence map›Paper›PMID 39747344›Full record

ArticleScientific reports2025

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models.

Abbas Pak, Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Mohammadreza Mahmoudi

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  3. Dietary Predictors of Paraben Exposure Among Adults in Northern Thailand.International journal of environmental research and public health · 2026
    Article
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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

4 authors.

Abbas PakDepartment of Computer Sciences, Shahrekord University, Shahrekord, Iran.
Abdullah Kaviani RadDepartment of Environmental Engineering and Natural Resources, College of Agriculture, Shiraz University, Shiraz, 71946-85111, Iran.
Mohammad Javad NematollahiDepartment of Geology, Faculty of Sciences, Urmia University, Urmia, 57561-51818, Iran. mj.nematollahi@urmia.ac.ir.
Mohammadreza MahmoudiDepartment of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran. mahmoudi.m.r@fasau.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM

Indexed as

Air pollutionAir quality predictionLasso regularisationMachine learningOverfitting

Identifiers

PMID39747344
PMCPMC11696743

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.