Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
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DOI：
10.1029/2019ms002002
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发表时间：
2019-12
影响因子：
6.8
通讯作者：
B. Toms;E. Barnes;I. Ebert‐Uphoff
中科院分区：
文献类型：
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作者：
B. Toms;E. Barnes;I. Ebert‐Uphoff
神经网络在地球科学中已经变得越来越普遍，尽管它们使用的一个共同限制是缺乏方法来解释网络学习什么以及它们如何做出决定。因此，在地球科学中，神经网络经常被用来在给定一组输入的情况下最准确地识别所需的输出，并将对网络学习的解释用作次要指标，以确保网络出于正确的原因做出正确的决定。然而，神经网络解释技术近年来变得更加先进，因此我们提出，使用神经网络的最终目标也可以是对网络学习的内容的解释，而不是输出本身。我们表明，对神经网络的解释可以在地学数据中发现具有科学意义的联系。特别是，我们使用了两种神经网络解释方法，称为反向优化和LayerWise相关性传播，这两种方法都将网络的决策路径投影到原始输入维度上。据我们所知，LRP还没有被应用于地球科学研究，我们相信它在这一领域具有巨大的潜力。我们展示了如何使用这些解释技术，通过将它们应用于常见的气候模式，从神经网络可靠地推断出有科学意义的信息。这些结果表明，将可解释神经网络与新的科学假设相结合，将为与神经网络相关的地学研究开辟许多新的途径。
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.