{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T08:42:50Z","timestamp":1783413770652,"version":"3.54.6"},"reference-count":80,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Deanship of Islamic University of Madinah","award":["641"],"award-info":[{"award-number":["641"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22093506","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:46:39Z","timestamp":1651805199000},"page":"3506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7050-0532","authenticated-orcid":false,"given":"Abdallah","family":"Namoun","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9683-8689","authenticated-orcid":false,"given":"Burhan Rashid","family":"Hussein","sequence":"additional","affiliation":[{"name":"School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4871-4080","authenticated-orcid":false,"given":"Ali","family":"Tufail","sequence":"additional","affiliation":[{"name":"School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6753-7684","authenticated-orcid":false,"given":"Ahmed","family":"Alrehaili","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"},{"name":"Department of Informatics, University of Sussex, Brighton BN1 9RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toqeer Ali","family":"Syed","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oussama","family":"BenRhouma","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","unstructured":"Worldbank (2022, April 08). 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