Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed
Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed
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用于估计全球内部孤立波速度的卫星数据驱动和知识型机器学习模型
DOI：
10.1016/j.rse.2022.113328
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发表时间：
2022-12
影响因子：
13.5
通讯作者：
Xiaofeng Li
中科院分区：
文献类型：
--
作者：
Xudong Zhang;Xiaofeng Li
内孤立波在世界范围内广泛分布，对海洋环境和近海活动有着重要影响。ISW传播速度对于ISW预测很重要，并且在全球范围内变化很大。本研究收集了全球13个热点地区的810幅具有清晰ISW特征的准同步光学卫星图像，构建了一个大型ISW数据集。利用提取的ISW波峰位置和卫星图像对之间的时间差计算ISW速度。该数据集包含57，196个样本，包括提取的ISW波峰和相应的ISW相速度。我们使用机器学习技术开发了一个基于数据集的ISW传播速度（IPS）模型。模型结构包括聚类和回归算法。该模型采用两个定制的修改，以纳入ISW领域的知识，并解决ISW样本分布不平衡的问题。实施领域知识（IDK）包括根据海洋学理论和遥感成像机制选择相关的海洋因子和ISW属性。第二个修改是采用先进的模型架构（AMA），通过引入高斯聚类算法将ISW样本分为几组，超越了空间和时间的限制。各组采用极值梯度Boosting回归算法建立IPS模型。我们使用47，425个样本作为训练数据集，其余9771个样本作为测试数据集。模型预测的ISW速度显示出良好的准确性，在训练和测试数据集上的均方根误差/相对误差率（RER）为0.16（7.9）和0.30 m/s（12.7%）。分析表明，IDK和AMA分别使模型性能提高了19.4%和13.1%。当输入参数的峰间距离存在一个像素的误差时，模型结果从0.30 m/s下降到0.33 m/s。应用IPS模型估算了13个热点以外海域的ISW速度，平均RER为6.0%。在7个海区进行了ISW预报试验，结果表明IPS模式能够较好地描述ISW的传播模式。模拟结果表明，ISW相速度与大潮和小潮有很强的相关性。IPS模式的结果表明，随着层结的加深，ISW的速度减小。模型预测的全球ISW传播速度的比较表明，西里伯斯海和南美洲西北部有最快和最慢的传播ISW全年，分别。讨论了背景电流对IPS模型计算结果的影响。
Internal solitary waves (ISW) are widely distributed worldwide and significantly affect the ocean environment and offshore activities. ISW propagation speed is important for ISW forecasts and varies largely globally. This study collected 810 quasi-synchronous optical satellite images with clear ISW signatures in 13 global hotspots to build a large ISW dataset. ISW speed was calculated using extracted ISW wave crest locations and the time difference between satellite image pairs. The dataset contains 57,196 samples, including extracted ISW wave crests and corresponding ISW phase speed. We developed an ISW propagation speed (IPS) model based on the dataset using machine learning techniques. The model structure includes clustering and regression algorithms. The model adopts two tailored modifications to incorporate the ISW domain knowledge and solve the ISW sample distribution imbalance problems. Implementation domain knowledge (IDK) includes selecting relevant ocean factors and ISW properties based on oceanography theory and remote sensing imaging mechanisms. The second tailored modification is adopting advanced model architecture (AMA) by introducing the Gaussian clustering algorithm to classify ISW samples into several groups beyond the limitation of space and time. The extreme gradient boosting regression algorithm was applied in each group to build the IPS model. We used 47,425 samples as the training dataset and the remaining 9771 samples as the test dataset. The model-predicted ISW speed shows good accuracy, with a root mean square error/relative error rate (RER) of 0.16 (7.9) and 0.30 m/s (12.7%) on the training and test dataset. Analysis shows that IDK and AMA improve the model performance by 19.4% and 13.1%, respectively. With a one-pixel error in the peak-to-peak distance of input parameters, the model results degraded from 0.30 m/s to 0.33 m/s. The IPS model was applied to estimate ISW speeds in ocean regions besides the 13 hotspots, and the average RER is 6.0%. ISW forecast in seven ocean areas was tested, and the results indicate that the IPS model can describe ISW propagation patterns. The model results reveal that the ISW phase speed strongly correlates with the spring and neap tide. The IPS model results show that ISW speed is decreased with a deepening stratification. Model-predicted global ISW propagation speed comparison shows that the Celebes Sea and North-West of South America has the fastest and slowest propagating ISWs all year around, respectively. Discussion on the background current's influence on the IPS model results is presented.
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影响因子：
3.5
作者：
Yunchao Yang;Xiaodong Huang;Wei Zhao;Chun Zhou;Siwei Huang;Zhiwei Zhang;Jiwei Tian
通讯作者：
Yunchao Yang;Xiaodong Huang;Wei Zhao;Chun Zhou;Siwei Huang;Zhiwei Zhang;Jiwei Tian
影响因子：
18.3
作者：
A. Wyatt;J. Leichter;L. Toth;T. Miyajima;R. Aronson;T. Nagata
通讯作者：
A. Wyatt;J. Leichter;L. Toth;T. Miyajima;R. Aronson;T. Nagata
影响因子：
13.5
作者：
I. Kozlov;D. Romanenkov;A. Zimin;B. Chapron
通讯作者：
I. Kozlov;D. Romanenkov;A. Zimin;B. Chapron
影响因子：
--
作者：
Q. Zheng;R. Susanto;Chung‐Ru Ho;Y. Song;Qing Xu
通讯作者：
Q. Zheng;R. Susanto;Chung‐Ru Ho;Y. Song;Qing Xu
影响因子：
0.2
作者：
Xiaofeng Li;P. Clemente-Colon;K. Friedman
通讯作者：
Xiaofeng Li;P. Clemente-Colon;K. Friedman