可解释的AI和基于机器学习的城市固体废物产生率分析:南非的案例研究
Oluwatobi Adeleke1, Tien-Chien Jen1
1Mechanical Engineering Science, University of Johannesburg, Johannesburg. South Africa.
Waste management (New York, N.Y.)
|August 2, 2025
概括
本研究引入了一种新的机器学习框架,通过分析复杂因素来理解和预测固体废物产生的情况. 它揭示了垃圾清除获取和收入等关键驱动因素,有助于制定有针对性的政策.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 固体废物产生是一个日益增长的全球挑战,需要可持续的管理策略.
- 现有的废物预测机器学习 (ML) 模型往往忽视了多因素复杂性,多对线性,可解释性和区域差异.
- 这项研究解决了对废物产生分析更全面,更易于解释的方法的需求.
研究的目的:
- 开发和验证一个多阶段的ML框架,用于分析复杂的废物产生模式.
- 确定废物产生的主要社会经济,人口,气象和基础设施驱动因素.
- 为市政规划人员提供可操作的见解,以优化废物管理政策和资源分配.
主要方法:
- 整合主要组件分析 (PCA) 以减少维度.
- 应用k-means聚类来识别不同的废物产生配置文件.
- 使用夏普利添加式解释 (SHAP) 进行模型解释性.
- 适应性神经模糊推理系统 (ANFIS) 的开发,用于预测建模.
主要成果:
- PCA有效地降低了数据的复杂性,在13个主要组件中保持了90.3%的差异.
- 基于服务访问和基础设施水平,K-means集群确定了3个不同的组.
- SHAP分析强调了废弃物清除的准入,相对湿度,人口密度和家庭收入作为重要的预测因素.
- 通过PCA和网格分区优化的ANFIS模型实现了高预测准确性 (R2 = 0.8943).
结论:
- 开发的ML框架为分析复杂的废物产生数据提供了一个强大的和可解释的方法.
- 关键的社会经济和环境因素显著影响废物产生,需要量身定制的管理策略.
- 这些发现支持数据驱动的决策,以制定有针对性的政策和优化城市废物管理中的资源.
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