دورية أكاديمية
Living review framework for better policy design and management of hazardous waste in Australia.
| العنوان: | Living review framework for better policy design and management of hazardous waste in Australia. |
|---|---|
| المؤلفون: | Le-Khac UN; Data Science and AI Department, Faculty of Information Technology, Monash University, Australia. Electronic address: uyennhulekhac@gmail.com., Bolton M; Monash Sustainable Development Institute, Monash University, Australia., Boxall NJ; Environment, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia., Wallace SMN; Centre for Anthropogenic Pollution Impact and Management (CAPIM), School of BioSciences, University of Melbourne, Australia., George Y; Data Science and AI Department, Faculty of Information Technology, Monash University, Australia. |
| المصدر: | The Science of the total environment [Sci Total Environ] 2024 May 10; Vol. 924, pp. 171556. Date of Electronic Publication: 2024 Mar 07. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Original Publication: Amsterdam, Elsevier. |
| مستخلص: | The significant increase in hazardous waste generation in Australia has led to the discussion over the incorporation of artificial intelligence into the hazardous waste management system. Recent studies explored the potential applications of artificial intelligence in various processes of managing waste. However, no study has examined the use of text mining in the hazardous waste management sector for the purpose of informing policymakers. This study developed a living review framework which applied supervised text classification and text mining techniques to extract knowledge using the domain literature data between 2022 and 2023. The framework employed statistical classification models trained using iterative training and the best model XGBoost achieved an F1 score of 0.87. Using a small set of 126 manually labelled global articles, XGBoost automatically predicted the labels of 678 Australian articles with high confidence. Then, keyword extraction and unsupervised topic modelling with Latent Dirichlet Allocation (LDA) were performed. Results indicated that there were 2 main research themes in Australian literature: (1) the key waste streams and (2) the resource recovery and recycling of waste. The implication of this framework would benefit the policymakers, researchers, and hazardous waste management organisations by serving as a real time guideline of the current key waste streams and research themes in the literature which allow robust knowledge to be applied to waste management and highlight where the gap in research remains. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Australia; Hazardous waste; Keyword extraction; Text mining; Topic modelling; Waste management |
| تواريخ الأحداث: | Date Created: 20240308 Latest Revision: 20240402 |
| رمز التحديث: | 20240402 |
| DOI: | 10.1016/j.scitotenv.2024.171556 |
| PMID: | 38458450 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-1026 |
|---|---|
| DOI: | 10.1016/j.scitotenv.2024.171556 |