在中国沿海地区重建极端海平面,使用多种深度学习模型
Jiayi Fang1,2, Jionghao Huang3,4, Wanchao Bian3,4
1Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 311121, China. jyfang@hznu.edu.cn.
Scientific data
|January 19, 2026
概括
一个新的数据集重建了1970-2020年中国每日最大的沿海水位,使用深度学习来计算风暴潮和天文潮. 这些沿海水位数据有助于分析极端事件和减轻沿海危险.
科学领域:
- 海洋学 海洋学 海洋学
- 沿海工程 沿海工程
- 数据科学数据科学数据科学
背景情况:
- 准确的沿海水位数据对于了解和减轻沿海危险至关重要.
- 现有的数据集在空间覆盖或时间分辨率方面可能存在局限性,特别是在观测稀疏的地区.
研究的目的:
- 创建一个关于中国沿海每日最大沿海水位的综合数据集,涵盖1970年至2020年的中国沿海.
- 为了使极端沿海水位的时空空间分析,并支持沿海危险减轻的努力.
主要方法:
- 开发了一个基于Informer的深度学习模型来预测风暴浪潮残留物,与LSTM,CNN-LSTM和ConvLSTM进行了基准测试.
- 预测的风暴浪潮残留物与UTide估计的天文潮相结合.
- 利用ERA5再分析数据和多来源的潮计记录进行模型培训和验证.
主要成果:
- 重建的每日最大沿海水位达到0.81的平均相关系数和11.7厘米的RMSE.
- 对于极端事件 (第95百分位),平均相关性为0.68,RMSE通常低于20厘米.
- 在潮和浪潮的峰值之间,平均估计有14.9厘米 (14.8%) 的非巧合偏差.
结论:
- 发布的数据集为中国的沿海研究和管理提供了宝贵的资源.
- 深度学习方法提供了一种可靠的方法来重建历史沿海水位.
- 该数据集有助于改进沿海危险评估和缓解策略,特别是在数据稀缺的地区.
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