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将多变量场景与Attention-LSTM模型集成在一起,以预测长期沿海海侵蚀

Xuanhao Huang¹, Yangfan Li¹, Xinwei Wang¹

¹Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.

The Science of the total environment

|September 17, 2024

概括

沙侵蚀因气候变化和人类活动而恶化. 整合自然和人为因素改善了侵蚀预测,人口增长被认为是深的一个关键驱动因素.

科学领域:

环境科学环境科学
沿海的地形形态学
适应气候变化适应气候变化

背景情况:

海侵蚀构成重大威胁,气候变化和人类发展加剧了这一威胁.
现有的预测模型经常忽视人为因素,导致侵蚀评估不完整.
有效的海管理需要一个整体的方法,整合自然和人类的影响.

研究的目的:

通过结合自然和人为因素来提高海侵蚀预测.
为深的沙开发准确可靠的侵蚀预测.
确定沿海侵蚀的主要驱动因素,并为有效的管理策略提供信息.

主要方法:

利用CoastSat模型提取1986年至2020年的历史海岸线数据.
开发了使用注意力-LSTM模型预测侵蚀影响的多变量场景.
将注意力-LSTM组合方法与标准注意力-LSTM模型和数字海岸线分析系统 (DSAS) 进行了比较.

主要成果:

在过去的35年里,深的海经历了高达12米的侵蚀.
预计到本世纪中叶,人口增长 (21.0%) 将成为影响侵蚀率的主要因素.
注意-LSTM多模型组合表现出更好的准确性,平均不确定性为10.99,与DSAS的13.29相比.

结论:

整合自然和人为因素显著提高了海侵蚀预测的准确性.
人口增长和人类对海的需求是沿海侵蚀动态的关键因素.
这些发现支持实施综合沿海管理战略,包括生态保护,以减轻海侵蚀.

关键词:

注意-LSTM 的注意减轻海侵蚀的影响人类维度人类维度情景分析就是一种情景分析.

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