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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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