使用遥感,机器学习和数值建模的合成方法,预测甲在沿海海域的日常空间分布
Hai Li1, Xiuren Li1, Dehai Song2
1Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Key Laboratory of Physical Oceanography, Ministry of Education, Ocean University of China, Qingdao, China; Laoshan Laboratory, Qingdao, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China.
The Science of the total environment
|November 22, 2023
概括
一个新的水生地化学 (HBGC) 模型与卷积神经网络 (CNN) 结合,准确地预测了叶绿素-a (Chl-a) 水平,改善了有害藻类繁殖 (HAB) 的预测. 这种综合方法提高了沿海社区的预测准确性.
科学领域:
- 海洋学 海洋学 海洋学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 有害藻类繁殖 (HAB) 对沿海地区构成重大生态和经济挑战.
- 准确预测叶绿素-a (Chl-a) 对于预测HAB至关重要,但传统的水生地化学 (HBGC) 模型和纯数据驱动的方法存在局限性.
- 现有的模型在高时空分辨率的Chl-a预测方面遇到了困难.
研究的目的:
- 开发一种新的合水生地化学 (HBGC) 和卷积神经网络 (CNN) 模型,用于预测每日表面Chl-a分布.
- 为了提高HAB预测的Chl-a预测的精度和时空分辨率.
- 创建一个强大的模型,用于数据重建和Chl-a.的历史再分析.
主要方法:
- 开发了一种结合HBGC-CNN模型,将HBGC输出 (温度,盐度,营养素,动物浮游生物) 与遥感Chl-a数据相结合.
- 使用集成数据训练了CNN模型,以捕捉Chl-a的复杂时空动态.
- 验证了模型在复制每日和季节性Chl-a变化和分析特定HAB事件中的性能.
主要成果:
- 该HBGC-CNN模型成功地以高精度重现了每日和季节性Chl-a变化.
- 该模型有效地解释了与大雨引发的HAB事件相关的时空信息.
- 证明了模型的数据重建能力,产生无缺口的Chl-a产品,特别是在近海地区.
结论:
- 结合的HBGC-CNN模型在预测Chl-a和预测HABs方面取得了重大进展.
- 这种方法提高了在区域和全球范围内开发操作预测系统的潜力.
- 该模型可以减少HAB灾难的影响,并帮助应急响应工作.
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