Machine learning-based prediction of heating values in municipal solid waste.

Mansour Baziar, Mahmood Yousefi, Vahide Oskoei, Ahmad Makhdoomi, Reza Abdollahzadeh, Aliakbar Dehghan

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. Not yet cited in PubMed.

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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.

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2 · The registry

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mansour Baziar *Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran.

Mahmood Yousefi *Department of Environmental Health Engineering, School of Public Health, Khoy University of Medical Sciences, Khoy, Iran.

Vahide OskoeiSchool of Life and Environmental Science, Deakin University, Geelong, Australia.

Ahmad MakhdoomiDepartment of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.

Reza AbdollahzadehDepartment of Environmental Sciences and Policy, Central European University, Vienna, Austria.

Aliakbar DehghanDepartment of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. aliakbardehghan@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this research, our objective was to utilize different machine learning techniques, such as XGBoost, Extra Trees, CatBoost, and Multiple Linear Regression (MLR), to model the heating values of municipal solid waste. The input parameters considered for the constructed models included the weight of the dry sample (kg) and the content of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), sulfur (S), and ash in kg. The Extra Trees model, fine-tuned for hyperparameters, demonstrated outstanding performance, achieving R

Indexed as

Extra treesHeating valuesMachine learningMunicipal solid wastePrediction

Identifiers

PMID40287500

DOI10.1038/s41598-025-99432-8

PMCPMC12033275

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LicenceCC BY-NC-ND

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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.