Liu Tao, Guan Xin, Wang Zeyu, Qin Tianqiao, Sun Rui, Wang Yadong
School of Journalism and Communication, Guangzhou University, Guangzhou, 510006, China.
Guangzhou Xinhua University, Dongguan, 523133, China.
Heliyon. 2024 Apr 25;10(9):e29825. doi: 10.1016/j.heliyon.2024.e29825. eCollection 2024 May 15.
This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.
本文探讨了通过纳入机器学习技术来加强智能城市中绿色供应链循环经济整合的方法。为了提高预测模型的精度和有效性，引入引力算法来优化支持向量机模型中的参数选择。利用公共经济、环境和人口数据精心构建了一个全国性的绿色供应链经济发展效率预测模型。随后进行了全面的实证分析，结果显示随着迭代次数的增加，均方误差和均方根误差显著降低，分别达到最小值0.007和0.103，这是所有考虑的机器学习模型中最低的。此外，平均绝对百分比误差值非常低，为0.0923。数据表明在整个模型优化过程中平均预测误差和标准差逐渐下降，这表明模型收敛且预测准确性提高。这些结果强调了机器学习技术在优化供应链和循环经济管理方面的巨大潜力。本文为在可持续发展领域的决策者和研究人员提供了有价值的见解。
