使用物联网和机器学习进行主动城市废物管理的ProWaste
Thompson Stephan1, S M Hari Krishna2, Chia-Chen Lin3
1Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, UAE.
Scientific reports
|July 30, 2025
概括
智能废物管理平台ProWaste使用物联网和机器学习来预测垃圾收集中心的溢出情况. 这种数据驱动的方法优先考虑服务,改善城市可持续性和公共卫生.
科学领域:
- 环境科学与工程环境科学与工程
- 计算机科学和人工智能 人工智能
- 城市规划与可持续发展
背景情况:
- 城市垃圾收集中心 (WCCs) 经常因反应性维护计划而溢出,导致公共卫生风险并阻碍智能城市的可持续性.
- 来自WCC的溢出,气味和液对快速增长的城市环境构成重大挑战.
研究的目的:
- 推出ProWaste,一个物联网 (IoT) 和机器学习平台,旨在积极优先考虑WCC服务.
- 开发数据驱动的预测系统,以优化智能城市的废物管理操作.
主要方法:
- 从传感器,公共API和移动应用程序收集数据,包括人口密度,天气,维护历史和废物积累.
- 通过对比25个分类器,选择一个决策树分类器,以其可解释性和准确性的平衡.
- 使用二进制粒子群集优化 (BPSO) 进行特征选择和SHAP分析以实现模型可解释性.
主要成果:
- 一个通过BPSO识别的三特征模型,在预测WCC关键性时达到99%以上的准确性.
- ProWaste平台成功地将数据流和机器学习预测集成到可持续智能废物管理 (SSWM) 应用程序中.
- SHAP分析证实了预测模型的可解释性,增强了信任和理解.
结论:
- ProWaste为城市废物管理提供了一个主动的,数据驱动的解决方案,大大提高了效率并降低了运营成本.
- 与传统方法相比,该平台可以消除错过的接送,减少道路检查,并降低数据带宽要求.
- ProWaste架构可转移到其他城市,并可适应用于管理回收和堆肥流,促进更广泛的可持续废物管理.
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