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基于的空气质量监测网络优化使用NINP和贝叶斯最大

Ali Haddadi¹, Mohammad Reza Nikoo², Banafsheh Nematollahi³

¹Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.

Environmental science and pollution research international

|June 24, 2023

概括

设计一个有效的空气质量监测网络 (AQMN) 需要优化站点的位置. 这项研究使用先进的算法来平衡覆盖范围和信息不确定性,从而使CO,NO2和臭氧等污染物的监测策略更有效.

科学领域:

环境工程环境工程
环境科学环境科学
数据科学数据科学数据科学

背景情况:

有效的空气质量监测网络 (AQMN) 设计对于环境管理至关重要.
最佳的AQMN设计需要考虑站的相互依赖性和系统的不确定性.

研究的目的:

开发一种新的优化模型来设计一个有效的AQMN.
为了最大限度地实现空间覆盖,同时最大限度地减少信息不确定性和选择多余站的可能性.

主要方法:

使用非主导排序基因算法II (NSGA-II) 进行优化.
采用贝叶斯最大 (BME) 和超信息 (TE) 方法来生成站和评估信息.
整合了成员的模糊程度和非线性间隔数编程 (NINP) 来处理联合信息的不确定性.
应用了偏好排名组织的丰富评估方法 (PROMETHEE) 来选择帕雷托最佳解决方案.

主要成果:

该方法应用于洛杉矶,长和加利福尼亚州的阿纳海姆.
建议分别对CO,NO2和臭氧进行4,4和5个检测站.
实现了对CO (8.25x),NO2 (5.86x) 和臭氧 (4.75x) 的转信息 (TE) 的实质性降低.

结论:

建议的优化模型为AQMN设计提供了一个有效的框架.
该方法平衡了监测覆盖范围与信息冗余,从而使空气质量监测更有效.
该研究表明,随着监测站数量的减少,信息不确定性显著下降.

关键词:

空气质量空气质量贝叶斯最大 (BME) 是指贝叶斯最大.模糊的集合理论模糊的集合理论多个标准的决策 (MCDM)非线性间隔数编程 (NINP) 是一种非线性间隔编程.信息转移 (TE) 是指信息转移.

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