Machine-learning approaches in geo-environmental engineering: Exploring smart solid waste management

• Published in: Journal of environmental management 2023-03, Vol.330, p.117174-117174, Article 117174
• Main Authors: Lakhouit, Abderrahim, Shaban, Mahmoud, Alatawi, Aishah, Abbas, Sumaya Y.H., Asiri, Emad, Al Juhni, Tareq, Elsawy, Mohamed
• Format: Article
• Language: English
• Subjects: Algorithms; Cities; Domestic waste; Greenhouses gas; Linear Models; Machine Learning; Municipal solid wastes; Refuse Disposal - methods; Solid Waste - analysis; Time series analysis; Waste Management - methods
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2022.117174

Over the past few decades, increased attention has been paid to domestic waste (DW) generation. DW comprises a large percentage of municipal solid waste (MSW), and its handling and processing involves serious technical issues while also consuming a major portion of municipal budgets. The accurate estimation, prediction, and characterization of DW is an ongoing challenge for many cities, municipalities, and local governments as they strive to implement sustainable strategies for MSW. The main objective of the present study is to estimate and correctly predict DW quantities using machine-learning (ML) algorithms. Several different ML algorithms are used in the research, including linear regression, regression trees, Gaussian process regression, support vector machine, and autoregressive integrated moving average methods for time series analysis. Two case studies are presented in this paper. In the first, domestic waste data covering the period from 2010 to 2021 were collected from the Saudi and Bahrain authorities, and in the second, the domestic waste-generating behavior of a family of eleven members was followed for one month. The results show that the biodegradable and non-biodegradable wastes generated by the family were in the range of 1.7–7.9 kg and 0.0–2.0 kg, respectively, and promising outcomes were obtained using an appropriate selection of input predictors in conjunction with time series analysis. The trained models are validated and tested using several types of evaluation metrics, including calculated residuals, mean square error, root mean square error, and coefficient determination (R2-Score). The latter values are in the range of 0.67–0.85 for the training and testing datasets for many of the predicted waste quantities. The results obtained from the study show that these algorithms can be used to reduce the environmental, economic, and societal impacts of waste by designing a smart waste management engineering system. [Display omitted] •Importance of specific ML approaches in decision making in wastes management to reduce their impacts.•Several ML algorithms are tested and validated to estimate domestic wastes.•Applying specific ML algorithms to estimate and forecast the quantity of domestic wastes for the first time.