基于机器学习,预测城市固体废物产生和分析快速发展的城市的主导变量 - - 中国的案例研究
概括
准确预测城市固体废物 (MSW) 生产是有效废物管理的关键. 这项研究开发了一种反向传播神经网络模型,确定区域GDP和零售销售作为关键预测指标.
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:
- 有效的城市固体废物 (MSW) 管理依赖于准确的废物产生预测.
- 了解影响MSW产生的因素对于制定有针对性的管理策略至关重要.
研究的目的:
- 为城市固体废物产生 (MSWG) 开发准确的预测模型.
- 识别和分析主导变量对MSWG的影响.
- 为废物管理的政策制定者提供一个实际的工具.
主要方法:
- 在12个类别中选超过50个市政变量,以确定主导因素.
- 利用七种机器学习方法,专注于背向传播 (BP) 神经网络.
- 分析主导变量与MSWG之间的相关性.
主要成果:
- 在预测MSWG方面,BP神经网络表现出卓越的性能,具有高的R平方值 (例如,山东省的0.971).
- 预测准确性因各省而异,山东实现了93.8%的准确性.
- 区域GDP和消费品零售总额被确定为影响MSWG的最重要的变量.
结论:
- BP神经网络模型为MSWG预测提供了一种可靠的方法.
- 经济指标,而不仅仅是人口密度,是MSWG的主要驱动因素.
- 这些发现支持对区域和地方废物管理政策的知情决策.
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