Methodological Integration of Machine Learning and Geospatial Analysis for PM

Kalid Hassen Yasin, Muaz Ismael Yasin, Anteneh Derribew Iguala, Tadele Bedo Gelete, Erana Kebede

Abstract read

In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping.Scientific reports · 2025
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.

Kalid Hassen YasinGeo-Information Science Program, School of Geography and Environmental Studies, Haramaya University, P.O. Box 138, 3220 Dire Dawa, Ethiopia.

Muaz Ismael YasinSchool of Medicine, College of Health and Medical Sciences, Haramaya University, P.O. Box 235, Harar, Ethiopia.

Anteneh Derribew IgualaCenter for Rural Development, Oromia State University, Batu P.O. Box 209, Ethiopia.

Tadele Bedo GeleteGeo-Information Science Program, School of Geography and Environmental Studies, Haramaya University, P.O. Box 138, 3220 Dire Dawa, Ethiopia.

Erana KebedeSchool of Plant Sciences, College of Agriculture and Environmental Sciences, Haramaya University, P.O. Box 138, Dire Dawa, Ethiopia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Air pollution mitigation necessitates accurate spatial modelling to inform public health interventions. Traditional approaches inadequately capture complex predictor-pollutant interactions, whereas machine learning (ML) offers a superior capacity for modelling nonlinear relationships. This study compares three ML Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms using annual PM

Indexed as

Air PollutionData-driven approachNB, RF, and KNNPollutionSpatial prediction

Identifiers

PMID40331028

DOI10.1016/j.mex.2025.103322

PMCPMC12051153

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.