Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning
As global waste production grows, sustainable waste management (WM) has become an issue for modern societies. This paper explores the integration of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve waste management (WM) systems by enhancing automati...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-01-01
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| Series: | Watershed Ecology and the Environment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589471425000270 |
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| author | Son V.T. Dao Tuan M. Le Hieu M. Tran Hung V. Pham Minh T. Vu Tuan Chu |
| author_facet | Son V.T. Dao Tuan M. Le Hieu M. Tran Hung V. Pham Minh T. Vu Tuan Chu |
| author_sort | Son V.T. Dao |
| collection | DOAJ |
| description | As global waste production grows, sustainable waste management (WM) has become an issue for modern societies. This paper explores the integration of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve waste management (WM) systems by enhancing automation, classification accuracy, operational efficiency, and real-time decision-making. Current trends and potential future directions are identified with bibliometric and scientometric analysis, which assess methodologies and data in the field. By automating processes such as waste classification, sorting, and transportation, AI-driven models have the potential to optimize operational efficiency and reduce environmental impact. A comprehensive review of recent AI research in WM is presented, with a focus on their effectiveness, scalability, and limitations. Moreover, in the proposed framework, the data augmentation approach has been utilized to improve the model’s performance by increasing the amount of samples. Furthermore, the MobileNetV3 DL model is employed for feature extraction. Besides, the feature selection method − Harris Hawk Optimization (HHO) is also utilized to choose the best subset of features and reduce the irrelevant features. Then these selected features are fed into Machine Learning algorithms such as Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). In summary, this review highlights key case studies and research insights, offering a roadmap for future developments in AI-driven WM solutions. |
| format | Article |
| id | doaj-art-9c77cebf86894cf6bc1566cb4c4e484b |
| institution | Kabale University |
| issn | 2589-4714 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Watershed Ecology and the Environment |
| spelling | doaj-art-9c77cebf86894cf6bc1566cb4c4e484b2025-08-20T03:56:04ZengKeAi Communications Co., Ltd.Watershed Ecology and the Environment2589-47142025-01-01735338210.1016/j.wsee.2025.07.001Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learningSon V.T. Dao0Tuan M. Le1Hieu M. Tran2Hung V. Pham3Minh T. Vu4Tuan Chu5Corresponding author.; School of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City, Viet NamAs global waste production grows, sustainable waste management (WM) has become an issue for modern societies. This paper explores the integration of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve waste management (WM) systems by enhancing automation, classification accuracy, operational efficiency, and real-time decision-making. Current trends and potential future directions are identified with bibliometric and scientometric analysis, which assess methodologies and data in the field. By automating processes such as waste classification, sorting, and transportation, AI-driven models have the potential to optimize operational efficiency and reduce environmental impact. A comprehensive review of recent AI research in WM is presented, with a focus on their effectiveness, scalability, and limitations. Moreover, in the proposed framework, the data augmentation approach has been utilized to improve the model’s performance by increasing the amount of samples. Furthermore, the MobileNetV3 DL model is employed for feature extraction. Besides, the feature selection method − Harris Hawk Optimization (HHO) is also utilized to choose the best subset of features and reduce the irrelevant features. Then these selected features are fed into Machine Learning algorithms such as Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). In summary, this review highlights key case studies and research insights, offering a roadmap for future developments in AI-driven WM solutions.http://www.sciencedirect.com/science/article/pii/S2589471425000270Deep LearningWaste ManagementVehicle routing optimizationMachine learningArtificial Neural Network |
| spellingShingle | Son V.T. Dao Tuan M. Le Hieu M. Tran Hung V. Pham Minh T. Vu Tuan Chu Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning Watershed Ecology and the Environment Deep Learning Waste Management Vehicle routing optimization Machine learning Artificial Neural Network |
| title | Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning |
| title_full | Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning |
| title_fullStr | Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning |
| title_full_unstemmed | Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning |
| title_short | Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning |
| title_sort | integrating artificial intelligence for sustainable waste management insights from machine learning and deep learning |
| topic | Deep Learning Waste Management Vehicle routing optimization Machine learning Artificial Neural Network |
| url | http://www.sciencedirect.com/science/article/pii/S2589471425000270 |
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