通过ML提高门到门的垃圾收集预测
Luca Pasa1, Giuseppe Angelini2, Michele Ballarin2
1Department of Mathematics, University of Padova, Via Trieste, 63, Padova, 35121, Italy.
Waste management (New York, N.Y.)
|January 8, 2025
概括
机器学习 (ML) 模型可以预测门到门的垃圾收集需求,以实现有效的城市固体废物 (MSW) 管理. 这种方法优化了城市地区的收集路线和资源分配.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 城市规划 城市规划
背景情况:
- 城市固体废物 (MSW) 管理面临着挑战,因为城市人口和废物产生日益增加.
- 优化门到门的垃圾收集对于效率和可持续性至关重要.
- 现有的废物管理策略往往缺乏对动态收集需求的预测能力.
研究的目的:
- 研究机器学习 (ML) 技术的应用,用于预测门到门的垃圾收集.
- 预测用户层面的家庭废物收集需求.
- 评估ML算法在优化废物收集操作方面的有效性.
主要方法:
- 利用来自意大利东北部市镇的综合废物数据,包括各种废物类型.
- 开发了ML模型来预测每日废物暴露的可能性和预测完成的拾取 (每周/每月).
- 采用时间数据分割用于模型培训和评估,预测未来的用户行为.
主要成果:
- 机器学习模型在预测废物收集需求方面显示出显著的潜力.
- 该研究成功地预测了在颗粒级别上收集废物的用户行为.
- 确定了ML在提高废物收集效率和路线规划方面的有效性.
结论:
- 机器学习为优化城市固体废物收集提供了一个强大的工具.
- 针对特定废物类别和回收频率量身定制战略对于最佳的ML性能至关重要.
- 这些发现支持改善城市废物管理中的资源分配和环境可持续性.
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