沿海城市河流水质的动态模式和潜在驱动因素:基于机器学习的框架和水资源管理的见解
Yicheng Huang1, Shengyue Chen1, Xi Tang1
1Fujian Key Laboratory of Coastal Pollution Prevention and Control, Xiamen University, 361102, Xiamen, China.
Journal of environmental management
|October 15, 2024
概括
机器学习模型揭示了影响河水质量的关键转折点和驱动因素,包括化学氧气需求 (CODMn),氨 (NH3-N) 和总 (TP). 结果为沿海城市的水质管理提供了洞察力.
科学领域:
- 环境科学 环境科学
- 水质管理水质管理
- 机器学习应用 机器学习应用
背景情况:
- 由于气候变化和人类活动,河流水质量正在下降.
- 现有的机器学习研究不足以解决复杂的时空动态和河水质量的驱动因素.
- 了解这些动态对于有效的水资源管理至关重要.
研究的目的:
- 开发和应用一个集成的机器学习框架来分析河流水质量的时空模式.
- 确定影响酸盐 (CODMn),氨 (NH3-N) 和总 (TP) 动态的关键驱动因素.
- 为沿海城市环境的水资源管理策略提供见解.
主要方法:
- 开发了一个机器学习框架,结合了自组织地图 (SOM) 和随机森林 (RF) 模型.
- 应用框架分析河流水质数据 (CODMn,NH3-N,TP) 在34个地点从2010-2020年.
- 研究了时空变化,并确定了导致水质变化的因素.
主要成果:
- 空间分析揭示了两种不同的水质条件集群.
- 确定2015年为NH3-N和2018年为CODMn和TP的转折点.
- 污水排放,人口,耕地和肥料使用是导致水质恶化的主要因素,而森林植被显示出潜在的积极影响.
结论:
- 综合的ML框架有效地捕捉了河流水质的时空动态和驱动因素.
- 调查结果强调了人类活动和土地利用对水质的重大影响.
- 该研究为面临环境压力的沿海城市的有针对性的水资源管理提供了有价值的数据驱动的见解.
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