气候变化领域集成服务门户(CCPortal): An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns

CCPortal

DOI	10.3390/app14020601
	An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
	He, Qi; Zhu, Zihang; Zhao, Danfeng; Song, Wei; Huang, Dongmei
发表日期	2024
EISSN	2076-3417
起始页码	14
结束页码	2
卷号	14 期号:2
英文摘要	Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model's predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery.
英文关键词	sea surface temperature; marine heat waves; explainable artificial intelligence
语种	英语
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001149505300001
来源期刊	APPLIED SCIENCES-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301656
作者单位	Shanghai Ocean University; Shanghai University of Electric Power
推荐引用方式 GB/T 7714	He, Qi,Zhu, Zihang,Zhao, Danfeng,et al. An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns[J],2024,14(2).
APA	He, Qi,Zhu, Zihang,Zhao, Danfeng,Song, Wei,&Huang, Dongmei.(2024).An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns.APPLIED SCIENCES-BASEL,14(2).
MLA	He, Qi,et al."An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns".APPLIED SCIENCES-BASEL 14.2(2024).