Environmental planning based on reduce,reuse, recycle and recover using artificial intelligence |
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| Institution: | 1. School of Civil, Environmental and Mining Engineering, University of Western Australia, Perth, 6009, Australia;2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China;1. Department of Information Management, National Chi Nan University, 1, University Road, Pu-Li 545, Taiwan, ROC;2. Department of Accounting Information Management, Da-Yeh University, No. 168, University Rd., Dacun, Changhua 51591, Taiwan, ROC;1. School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong;2. College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq;3. College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq;4. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice 44100, Poland;5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia;6. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia |
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| Abstract: | Waste disposal was a significant challenge faced by the community and government. Customers buy and use goods that produce a considerable amount of waste. Waste management is a major problem since the number of consumers increased due to high waste generation. This has resulted in a huge amount of waste, which calls for enormous waste-management policies. Reduce; Reuse, Recycle, and Recover are the tools to reduce the adverse implications of retailing and manufacturing on the environment. In this paper, Artificial Intelligence based Hybridized Intelligent Framework (AIHIF) has been proposed for automated recycling to optimizing the waste management process. The system will optimize waste collection with a short distance by utilizing machine learning and graph theory. AI design technology, which helps different approaches adapted to interest groups, collecting their specific information and greatly improving environmental planning and urban management performance, accuracy, and efficiency. The experimental results show that the proposed method enhances performance and accuracy when compared to other existing methods. |
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