Evidence map›Paper›PMID 38571638›Full record

ArticleHeliyon2024

Estimating plastic waste generation using supervised time-series learning techniques in Johannesburg, South Africa.

Olusola Olaitan Ayeleru, Lanre Ibrahim Fajimi, Matthew Adah Onu, Tarhemba Tobias Nyam, Sisanda Dlova, Victor Idankpo Ameh, Peter Apata Olubambi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. 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
0.9field-weighted citation impact, top 28% of its field
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, 3 citations in OpenAlex.

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

7 authors at 1 institution in 1 country.

Olusola Olaitan AyeleruCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Lanre Ibrahim FajimiCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Matthew Adah OnuCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Tarhemba Tobias NyamCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Sisanda DlovaCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Victor Idankpo AmehCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
Peter Apata OlubambiCentre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa.
University of Johannesburg · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent times, many investigators have delved into plastic waste (PW) research, both locally and internationally. Many of these studies have focused on problems related to land-based and marine-based PW management with its attendant impact on public health and the ecosystem. Hitherto, there have been little or no studies on forecasting PW quantities in developing countries (DCs). The key objective of this study is to provide a forecast on PW generation in the city of Johannesburg (CoJ), South Africa over the next three decades. The data used for the forecasting were historical data obtained from Statistics South Africa (StatsSA). For effective prediction and comparison, three-time series models were employed in this study. They include exponential smoothing (ETS), Artificial Neural Network (ANN), and the Gaussian Process Regression (GPR). The exponential kernel GPR model performed best on the overall plastic prediction with a determination coefficient (R

Indexed as

ANNExponential smoothingGaussian process regressionPlastic wasteTime series model

Identifiers

PMID38571638
PMCPMC10987939
OpenAlexW4393165607

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.