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

Abderrahim Lakhouit; Mahmoud Shaban; Aishah Alatawi; Sumaya Y H Abbas; Emad Asiri; Tareq Al Juhni; Mohamed Elsawy

doi:10.1016/j.jenvman.2022.117174

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

J Environ Manage. 2023 Mar 15:330:117174. doi: 10.1016/j.jenvman.2022.117174. Epub 2022 Dec 29.

Authors

Abderrahim Lakhouit¹, Mahmoud Shaban², Aishah Alatawi³, Sumaya Y H Abbas⁴, Emad Asiri⁵, Tareq Al Juhni⁵, Mohamed Elsawy⁶

Affiliations

¹ Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk 71421, Saudi Arabia. Electronic address: a.lakhouit@ut.edu.sa.
² Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt; Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia.
³ Department of Biology, Faculty of Science, University of Tabuk, Tabuk 71421, Saudi Arabia.
⁴ Department of Natural Resources and Environment College of Graduate Studies Arabian Gulf University, Bahrain.
⁵ Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk 71421, Saudi Arabia.
⁶ Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk 71421, Saudi Arabia; Geotechnical and Foundations Engineering, Department of Civil Engineering, Faculty of Engineering, Aswan University, 81542, Egypt.

PMID: 36586367
DOI: 10.1016/j.jenvman.2022.117174

Abstract

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.

Keywords: Domestic waste; Greenhouses gas; Machine learning; Municipal solid wastes; Time series analysis.

MeSH terms

Algorithms
Cities
Linear Models
Machine Learning
Refuse Disposal* / methods
Solid Waste / analysis
Waste Management* / methods

Substances

Solid Waste