在排放点尺度上使用神经网络进行城市固体废物管理预测
Sergio De-la-Mata-Moratilla1, Jose-Maria Gutierrez-Martinez2, Ana Castillo-Martinez2
1Department of Computer Science, University of Alcala, 28801, Alcala de Henares, Spain. sergio.matam@uah.es.
Scientific reports
|February 2, 2026
概括
本研究引入了一个预测框架,用于预测单个废物排放点,从而实现更智能的城市废物管理. 数据驱动的本地化预测提高了收集效率并减少了对环境的影响.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 加快城市化和人口增长增加了城市固体废物 (MSW) 的产生,造成了重大的环境和后勤挑战.
- 目前的废物管理通常依赖于聚合数据,缺乏局部化,动态决策所需的细节性.
研究的目的:
- 开发一个预测框架来预测个别废物排放点 (DP) 的行为.
- 通过本地化和细分化的预测,加强城市废物管理决策.
- 通过提供准确的短期预测,实现积极的废物收集规划.
主要方法:
- 开发一个数据驱动的预测框架.
- 整合上下文和时间信息用于局部预测.
- 利用基于人工智能的预测模型进行废物行为分析.
主要成果:
- 该框架成功地预测了个别DP的行为,提供了更好的预测细度.
- 以数据为导向的方法,结合上下文和时间数据,提高废物收集规划.
- 准确的短期预测使人们能够从反应性转向主动性废物收集策略.
结论:
- 拟议的方法促进了更高效,可扩展和智能废物收集系统.
- 基于人工智能的预测模型对于推动可持续的MSW管理至关重要.
- 这项研究支持过渡到积极的城市废物管理,降低成本和环境足迹.
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