通过机器学习和增强的清洁水管理,设计城市中心的节能建筑
Ximo Chen1, Zhaojuan Zhang2, Azher M Abed3
1Zhejiang College of Security Technology, Wenzhou, 325000, China.
Environmental research
|July 7, 2024
概括
这项研究将银纳米粒子 (AgNPs) 集成到雨水收集 (RWH) 系统中,显著减少微生物含量,化学品使用和能源消耗. 一个混合SVM-KNN模型准确地预测了可持续城市环境的建筑能耗和视觉舒适度.
科学领域:
- 环境科学与工程环境科学与工程
- 材料科学 材料科学 材料科学
- 可持续的城市发展
背景情况:
- 雨水收集 (RWH) 对于城市的节水和能源节约至关重要.
- 传统的水处理方法对环境有影响,并且可能耗费大量能源.
- 需要先进的材料来提高RWH系统的效率和可持续性.
研究的目的:
- 将纳米复合材料的银纳米粒子 (AgNPs) 集成到RWH系统中.
- 评估RWH的AgNPs对环境和节能的好处.
- 评估支持矢量机 (SVM) 和K-近邻 (KNN) 模型对建筑性能的预测准确度.
主要方法:
- 在RWH系统中实施AgNP.
- 使用支持向量机 (SVM) 和K-最近邻近 (KNN) 算法进行回归分析.
- 输入变量包括建筑属性,环境参数和社会人口统计因素.
主要成果:
- 集成AgNP实现了57%的微生物含量减少.
- 化学品的使用和能源消耗减少了20%.
- 混合SVM-KNN模型在预测能源消耗和视觉舒适性方面表现出高准确度.
结论:
- 农业天然气资源提供了显著的潜力,以提高水安全,并减少RWH系统的环境足迹.
- 该研究验证了AgNPs作为城市环境中可持续水资源管理的可行替代方案.
- 混合SVM-KNN模型是优化城市建筑可持续性和乘客舒适性的有效工具.
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