Lakhouit Abderrahim, Shaban Mahmoud, Alatawi Aishah, Abbas Sumaya Y H, Asiri Emad, Al Juhni Tareq, Elsawy Mohamed
Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk 71421, Saudi Arabia.
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.
J Environ Manage. 2023 Mar 15;330:117174. doi: 10.1016/j.jenvman.2022.117174. Epub 2022 Dec 29.
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 (R-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.
在过去几十年里，人们对生活垃圾（DW）的产生给予了更多关注。生活垃圾在城市固体废物（MSW）中占很大比例，其处理和加工涉及严重的技术问题，同时还消耗了城市预算的很大一部分。对于许多城市、市镇和地方政府来说，准确估计、预测和描述生活垃圾仍然是一项持续的挑战，因为它们致力于实施城市固体废物的可持续战略。本研究的主要目的是使用机器学习（ML）算法估计并正确预测生活垃圾量。研究中使用了几种不同的ML算法，包括线性回归、回归树、高斯过程回归、支持向量机以及用于时间序列分析的自回归积分移动平均方法。本文介绍了两个案例研究。第一个案例中，从沙特和巴林当局收集了2010年至2021年期间的生活垃圾数据，第二个案例中，对一个十一口之家的生活垃圾产生行为进行了为期一个月的跟踪。结果表明，该家庭产生的可生物降解和不可生物降解废物分别在1.7 - 7.9千克和0.0 - 2.0千克范围内，通过结合时间序列分析适当选择输入预测变量，获得了有前景的结果。使用几种类型的评估指标对训练好的模型进行验证和测试，包括计算残差、均方误差、均方根误差和决定系数（R值）。对于许多预测垃圾桶数量的训练和测试数据集，后者的值在0.67 - 0.85范围内。研究结果表明，通过设计智能废物管理工程系统，这些算法可用于减少废物对环境、经济和社会的影响。
