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一个集机器学习和时间频率分析的多目标优化模型,用于支持在中国广州市减少和污染

Yelin Wang¹, Yanpeng Cai¹, Shunyu Zhao¹

¹Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China.

Journal of environmental management

|May 6, 2025

概括

一个新的模型优化了工业结构,以减少和 (NP) 污染,平衡经济增长与生态需求. 这种方法显著降低了NP度和极端污染事件.

科学领域:

环境科学环境科学
水质管理水质管理
计算机建模计算建模

背景情况:

过度的和 (NP) 排放威胁着全球生态系统的弹性.
解决NP污染需要理解与经济和环境因素的复杂相互作用.

研究的目的:

为NP污染减轻开发一个多目标优化模型.
集成先进的优化,时间频率分析和机器学习用于水质建模.
分析气候变化和工业结构对NP污染的影响.

主要方法:

开发了一个整体建模框架,集成优化,时间频率分析和机器学习.
在多个时间尺度上模拟了驱动力和NP污染变化之间的非线性关系.
将气候变化和工业结构调整因素纳入分析.

主要成果:

该模型有效地捕捉了水面系统的复杂性和不确定性.
优化工业结构调整可以减少NP污染,同时维持经济增长.
广州案例研究:预计到2025年,平均NP度将减少7.10%,极端污染频率将减少52.57%

结论:

开发的模型为协调经济发展与生态要求提供了有价值的工具.
季度工业生产结构调整可以有效地减轻过渡环境中的NP污染.
该方法支持NP污染的减少,并减少极端污染事件的频率.

关键词:

工业生产结构工业生产结构机器学习是机器学习.多目标优化多目标优化减少和污染减少污染时间频率分析.

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