一个基于深度学习的模型,用于估计从河流流向海洋的污染流,并对其进行优化
Liting Yin1, Wei Zheng2, Honghua Shi3
1College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China.
The Science of the total environment
|August 11, 2024
概括
流入海洋的河流污染严重影响沿海地区. 这项研究开发了一种深度学习模型,以估计污染流动,确定农村生活和牲畜等关键驱动因素,并确定高风险区域,以更好地管理生态系统.
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:
- 河流污染是近岸海洋环境中污染物的主要来源.
- 了解污染流的时空动态对于沿海生态系统的健康至关重要.
研究的目的:
- 估计海洋中的污染流量,并分析它们的时空空间异质性.
- 开发一个深度学习模型来简化污染流量估计.
- 为了确定不同空间梯度的污染贡献率.
主要方法:
- 采用了盆地-河口-沿海水系统的源-水槽过程模型.
- 开发了一个包含社会经济和气象数据的深度学习模型.
- 基于空间因素,提出了一种估计污染流量贡献率的方法.
主要成果:
- 博海海边盆地的总和流量在1980年至2020年之间呈现出差异.
- 农村生活和牲畜被确定为总和污染的主要贡献者.
- 深度学习模型在估计排放污染流量时达到90%以上的准确性.
- 距离海拔0-100米,距离海岸50-100公里,以及沿海地区的地区显示了最高的污染贡献率.
结论:
- 污染流量受到人类经济活动和环境政策的重大影响.
- 开发的深度学习模型提供了一种简化和准确的方法来估计污染流.
- 确定高贡献地区为近岸生态系统的适应性管理提供了关键数据.
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