可解释和因果机器学习研究香港沿海水质的时空动态模式
Hanwen Zhang1, Yiyan Li1, Mengyao Li1
1Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong SAR, China; The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, China.
Water research
|November 30, 2025
概括
这项研究揭示了城市,航运和农业活动如何影响沿海水质制度. 因果分析显示,人类影响正在不断演变,自然缓冲器正在减弱,为可持续的沿海管理提供了工具.
科学领域:
- 海洋科学 海洋科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 沿海水质是由自然过程和人类活动之间的复杂相互作用所塑造的.
- 解开这些影响对于有效的环境管理至关重要.
- 现有的方法往往难以分离特定驱动因素的因果影响.
研究的目的:
- 开发和应用一个可解释的,数据驱动的框架来研究长期沿海水质动态.
- 解开自然过程和人类活动对水质的影响.
- 为以可持续发展为导向的沿海治理提供可转移的工具.
主要方法:
- 集成集群,使用SHAP的可解释机器学习 (XML) 和因果推理 (CausalForestDML).
- 来自香港76个监测站的36年月度水质数据的分析.
- 识别不同的水质制度,估计人类驱动因素的因果关系.
主要成果:
- 确定了三种水质制度:低污染,微生物和环保.
- SHAP分析将微生物污染与城市邻近,清洁水与航运,营养丰富与农业联系起来.
- 因果建模显示,城市/航运对溶解氧的影响减弱,但农业对营养的影响加剧.
结论:
- 结合预测和因果分析,提供了对水质演变的机制性见解.
- 人类驱动因素的影响是动态的,农业影响加剧,自然缓冲器减弱.
- 该框架为沿海水质监管和可持续治理提供了有价值的工具.
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