Hybrid data-driven assessment and optimization strategies for municipal solid waste generation.

Prakhash Neelemagam, G Shyamala, K R Aswin Sidhaarth, Vigneshkannan Suresh, Missgna Addisalem Berhe

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

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

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

5 authors.

Prakhash NeelemagamDepartment of Civil Engineering, School of Engineering, SR University, Warangal, Telangana, 506371, India.ORCID http://orcid.org/0000-0002-2265-9012

G ShyamalaDepartment of Civil Engineering, School of Engineering, SR University, Warangal, Telangana, 506371, India.ORCID http://orcid.org/0000-0001-9928-440X

K R Aswin SidhaarthDepartment of Civil Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600 062, India.

Vigneshkannan SureshDepartment of Civil Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, India.ORCID http://orcid.org/0000-0001-9947-0675

Missgna Addisalem BerheDepartment of Civil Engineering, College of Engineering and Technology, Adigrat University, Adigrat, Ethiopia. missgna.addisalem@adu.edu.et.ORCID http://orcid.org/0009-0003-7616-0975

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a comprehensive, harmonized dataset and an interpretable analytical framework for assessing per-capita waste generation (PCWG) across 36 Indian states and union territories. The research employs a robust data preprocessing methodology combined with a hybrid feature-selection approach, incorporating mutual information, tree-based importance metrics, and SHAP-based explainability, ensuring a transparent identification of key influencing factors. Supervised machine-learning models, including CatBoost and Random Forest, were trained to capture complex nonlinear relationships in municipal solid waste data, achieving high coefficients of determination (R

Indexed as

Bayesian optimizationData preprocessingExplainable machine learningMulti-objective optimizationPer capita waste generation

Identifiers

PMID42135398

DOI10.1038/s41598-026-53202-2

PMCPMC13369995

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

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 OpenQuestion graph.