在智能城市中优化绿色供应链循环经济,采用集成机器学习技术
Tao Liu1, Xin Guan2, Zeyu Wang3
1School of Journalism and Communication, Guangzhou University, Guangzhou, 510006, China.
Heliyon
|May 10, 2024
概括
本研究介绍了机器学习和引力算法,以优化绿色供应链的循环经济预测在智能城市. 该模型实现了最小的误差,证明了可持续发展的提高准确性.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 经济学 经济学 经济学
背景情况:
- 整合绿色供应链和循环经济原则对于智慧城市的可持续性至关重要.
- 需要准确的预测模型来指导经济发展效率.
研究的目的:
- 通过机器学习增强绿色供应链,在智能城市中整合循环经济.
- 用引力算法优化预测模型参数以提高准确性.
主要方法:
- 使用经济,环境和人口统计数据开发了一个全国性的预测模型.
- 通过引力算法优化使用的支持向量机器 (SVM).
- 进行实证分析以评估模型性能.
主要成果:
- 实现了最小的平均平方误差 (0.007) 和根平均平方误差 (0.103).
- 记录了0.0923.3的低平均绝对百分比误差.
- 随着模型优化,观察到预测误差和标准偏差的减少,表明趋同和准确性.
结论:
- 机器学习,特别是用引力算法优化的SVM,显著提高了对绿色供应链循环经济效率的预测准确性.
- 该模型为可持续发展决策提供了有价值的见解.
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