沿海水质动态的时空模式和驱动因素:来自中国门湾可解释机器学习和PLS-SEM分析的见解
Aynuddin1, Yao Chen2, Jielong Xu3
1College of the Environment and Ecology, Xiamen University, Xiamen, China; AKA Bogor Polytechnic, Ministry of Industry, Bogor, Indonesia.
Marine pollution bulletin
|January 15, 2026
概括
城市地区有效的沿海水质管理是复杂的. 这项研究使用先进的方法来识别模式,确定土地使用变化等驱动因素,并确认综合沿海管理对可持续城市湾健康的好处.
科学领域:
- 环境科学 环境科学
- 海洋生物学 海洋生物学
- 数据科学数据科学数据科学
背景情况:
- 由于人类的压力和陆海相互作用,城市化地区难以管理沿海水质.
- 生态变化是由动态环境因素和日益增长的人类活动驱动的.
研究的目的:
- 开发一种综合方法来分析时空水质模式,并确定城市沿海地区的关键驱动因素.
- 评估环境因素,人类活动和水质之间的因果关系.
- 评估综合沿海管理 (ICM) 和海洋功能区划 (MFZ) 等管理策略的有效性.
主要方法:
- 自组织地图 (SOM) 用于空间时间水质模式的识别.
- 机器学习算法 (LightGBM) 和可解释的AI (SHAP) 来确定预测驱动器.
- 部分最小平方结构方程建模 (PLS-SEM) 来分析因果路径.
主要成果:
- SOM确定了两个不同的水质集群,在2012-2018年之间发生了转折点,与沿海复苏和社会经济变化有关.
- 轻GBM实现了>94%的准确性和0.88 F1得分,超过了其他11个算法.
- 土地覆盖的变化,工业排放和社会经济因素被确定为水质变化的主要驱动因素.
结论:
- 该研究提供了数据驱动的洞察力,用于快速城市化湾区的可持续沿海水资源管理.
- 像ICM和MFZ这样的积极管理框架证明了对水质的积极影响.
- 将模式识别与因果解释相结合,对于有效的沿海管理至关重要.
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