Evidence map›Paper›PMID 41484414›Full record

ArticleScientific reports2026

Towards intelligent air quality forecasting using integrated machine learning framework with variational mode decomposition and catboost feature selection.

Iman Ahmadianfar, Zaher Mundher Yaseen, Haydar Abdulameer Marhoon, Bijay Halder, Mou Leong Tan, Huseyin Cagan Kilinc, Sani I Abba, Salim Heddam, Leonardo Goliatt, Vahdettin Demir and 1 more

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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.

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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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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

11 authors.

Iman AhmadianfarDepartment of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.
Zaher Mundher YaseenInterdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia. z.yaseen@kfupm.edu.sa.
Haydar Abdulameer MarhoonCollege of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq.
Bijay HalderDepartment of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
Mou Leong TanGeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Minden, 11800, Penang, Malaysia.
Huseyin Cagan KilincCivil Engineering Department, Istanbul Aydın University, Istanbul, Turkey.
Sani I AbbaDepartment of Civil Engineering, Prince Mohammad Bin Fahd University, 31952, Al Khobar, Saudi Arabia.
Salim HeddamFaculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria.
Leonardo GoliattDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.
Vahdettin DemirDept. of Civil Engineering, KTO Karatay University, 42020, Konya, Turkey.
Ahmed M Al-AreeqCivil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting air pollution is crucial in improving air quality (AQ), which consequently provides benefits to the ecosystems and human health. AQ predictions often make use of Machine Learning (ML) approaches; nevertheless, these methods are not without their limitations. The main contribution of this research is to develop an efficient framework using machine learning (ML) for forecasting daily air quality metrics for Sulfur dioxide (SO

Indexed as

Air qualityCatboost methodKernel extreme learning machineLocally weightedMultivariate variational mode decomposition

Identifiers

PMID41484414
PMCPMC12852925

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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.