气候变化领域集成服务门户(CCPortal): Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

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DOI	10.1029/2019MS002002
	Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
	Toms B.A.; Barnes E.A.; Ebert-Uphoff I.
发表日期	2020
ISSN	19422466
卷号	12 期号:9
英文摘要	Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research. © 2020. The Authors.
英文关键词	climate; geoscience; interpretable machine learning; layerwise relevance propagation; neural networks; pattern discovery
语种	英语
scopus关键词	Backpropagation; Climate patterns; Earth systems; Geosciences; Geoscientific data; Input dimensions; Layer-wise; Scientific Hypothesis; Multilayer neural networks; accuracy assessment; artificial neural network; detection method; optimization
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156647
作者单位	Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States; Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United States
推荐引用方式 GB/T 7714	Toms B.A.,Barnes E.A.,Ebert-Uphoff I.. Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability[J],2020,12(9).
APA	Toms B.A.,Barnes E.A.,&Ebert-Uphoff I..(2020).Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability.Journal of Advances in Modeling Earth Systems,12(9).
MLA	Toms B.A.,et al."Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability".Journal of Advances in Modeling Earth Systems 12.9(2020).