先知时间序列建模了北美四个城市的废物处理率
Mohammad Mehedi Hasan1, Kelvin Tsun Wai Ng2, Sagar Ray1
1Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, 3737 Wascana Parkway, Regina, Saskatchewan, S4S 0A2, Canada.
Environmental science and pollution research international
|April 17, 2024
概括
在预测城市固体废物 (MSW) 处理率方面,先知模型总体上表现优于SARIMA和霍尔特-温特斯. 虽然在COVID-19前有效,但所有模型都在与大流行引起的处置行为变化作斗争.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 废物管理 废物管理
背景情况:
- 准确预测城市固体废物 (MSW) 处置对于有效的资源管理和环境规划至关重要.
- 现有的单变量时间序列模型 (SARIMA,Holt-Winters,Prophet) 已知能够处理季节性,但它们在异常值和流行病等前所未有的事件方面的表现不太清楚.
研究的目的:
- 评估和比较三个单变量模型 (SARIMA,Holt-Winters,Prophet) 的性能,以预测北美各个城市的MSW处理率.
- 评估模型在处理季节性和异常值方面的稳定性,特别是在COVID-19流行病背景下.
主要方法:
- 用四个北美城市的历史MSW处置数据对SARIMA,Holt-Winters和Prophet模型进行比较分析.
- 模型性能评估使用诸如平均绝对百分比误差 (MAPE) 和R平方 (R2) 等指标,跨越不同的测试期,包括COVID-19前和与大流行有关的中断期间.
主要成果:
- 与SARIMA和Holt-Winters相比,Prophet模型表现出更高的性能,在COVID-19前实现了4.3-22.2%的MAPE和0.71-0.93.2%的R2.
- 所有模型在COVID-19之前都提供了令人满意的预测,但它们难以准确地捕捉由流行病锁定和法规引起的MSW处置行为的突然变化.
- 先知模型的错误均分布,偏差最小,在广泛的废物处理量 (每月3000至39000) 中展示了多功能性.
结论:
- 单变量模型,特别是Prophet,显示了MSW处置预测的巨大潜力,在正常条件下提供了多功能性和强大的性能.
- 由于COVID-19大流行,当前的单变模型在适应由于外部冲击造成的废物产生模式的突然变化方面存在局限性.
- 对混合模型或自适应算法的进一步研究可能是必要的,以提高在前所未有的事件期间预测的准确性.
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