Machine learning analysis of Iran's wildfire landscape and anthropogenic influences.

Nasim Sadra, Mohammad Reza Nikoo, Rouzbeh Nazari, Maryam Karimi, Md Galal Uddin, Amir H Gandomi

Abstract read

In one paragraph

Article in Scientific reports, 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

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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.

Nasim SadraCollege of Sciences, School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand.

Mohammad Reza NikooCollege of Engineering, Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman. m.reza@squ.edu.om.

Rouzbeh NazariDepartment of Civil, Construction and Environmental Engineering, The University of Memphis, Memphis, United States, 38152.

Maryam KarimiSchool of Public Health, University of Memphis, Memphis, United States, TN, 38152.

Md Galal UddinCollege of Science and Engineering, University of Galway, Galway, Ireland.

Amir H GandomiDepartment of Engineering and I.T., University of Technology Sydney, Ultimo, NSW, 2007, Australia. gandomi@uni-obuda.hu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wildfires pose significant challenges globally, including in Iran. This study analyzes wildfire occurrences in Iran from 2001 to 2022, using NASA FIRMS' active fire detections MCD14DL data. To enhance the reliability of this satellite-based dataset, particularly in data-scarce regions like Iran, we applied a multi-sensor cross-validation framework before modeling. We also aim to examine the country's wildfire dynamics over two decades, employing k-means clustering to categorize wildfires into ten clusters, delineating fire zones. Two random forest regression models explore the relationships between annual CO

Indexed as

Carbon dioxide emissionsClimate variabilityEnvironmental mitigation strategiesMulti-sensor cross-validationNASA fire information for resource management system (FIRMS)Wildfire occurrences

Identifiers

PMID41495092

DOI10.1038/s41598-025-22387-3

PMCPMC12780200

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LicenceCC BY

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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.