Cargando…

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste manageme...

Descripción completa

Detalles Bibliográficos
Autores principales: Namoun, Abdallah, Hussein, Burhan Rashid, Tufail, Ali, Alrehaili, Ahmed, Syed, Toqeer Ali, BenRhouma, Oussama
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104882/
https://www.ncbi.nlm.nih.gov/pubmed/35591195
http://dx.doi.org/10.3390/s22093506
_version_ 1784707903264915456
author Namoun, Abdallah
Hussein, Burhan Rashid
Tufail, Ali
Alrehaili, Ahmed
Syed, Toqeer Ali
BenRhouma, Oussama
author_facet Namoun, Abdallah
Hussein, Burhan Rashid
Tufail, Ali
Alrehaili, Ahmed
Syed, Toqeer Ali
BenRhouma, Oussama
author_sort Namoun, Abdallah
collection PubMed
description With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.
format Online
Article
Text
id pubmed-9104882
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91048822022-05-14 An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation Namoun, Abdallah Hussein, Burhan Rashid Tufail, Ali Alrehaili, Ahmed Syed, Toqeer Ali BenRhouma, Oussama Sensors (Basel) Article With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed. MDPI 2022-05-05 /pmc/articles/PMC9104882/ /pubmed/35591195 http://dx.doi.org/10.3390/s22093506 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Namoun, Abdallah
Hussein, Burhan Rashid
Tufail, Ali
Alrehaili, Ahmed
Syed, Toqeer Ali
BenRhouma, Oussama
An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title_full An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title_fullStr An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title_full_unstemmed An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title_short An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation
title_sort ensemble learning based classification approach for the prediction of household solid waste generation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9104882/
https://www.ncbi.nlm.nih.gov/pubmed/35591195
http://dx.doi.org/10.3390/s22093506
work_keys_str_mv AT namounabdallah anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT husseinburhanrashid anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT tufailali anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT alrehailiahmed anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT syedtoqeerali anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT benrhoumaoussama anensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT namounabdallah ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT husseinburhanrashid ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT tufailali ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT alrehailiahmed ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT syedtoqeerali ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration
AT benrhoumaoussama ensemblelearningbasedclassificationapproachforthepredictionofhouseholdsolidwastegeneration