Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability (1912.01752v2)

Abstract: 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 backwards 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.

• The paper demonstrates that integrating LRP and Backwards Optimization makes neural network predictions in geoscience data physically interpretable.
• The study reveals these methods accurately differentiate climate patterns, including distinct ENSO phases and seasonal temperature trends.
• The research highlights that balancing model complexity with interpretability enables transparent, scientifically valuable insights into Earth system variability.

Insights into Physically Interpretable Neural Networks in Geosciences

The paper "Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability" explores the emerging application of neural networks within geosciences and especially focuses on interpretability—a significant challenge in their widespread adoption. With neural networks often termed as "black boxes," their integration into fields requiring scientific rigor has been tentative due to the opaque nature of their decision-making processes. This paper, by Toms, Barnes, and Ebert-Uphoff, offers a methodological contribution to geoscience, elucidating how neural network interpretability can be harnessed to unearth scientifically meaningful connections within geoscientific data.

The authors introduce two prominent interpretability techniques, Layerwise Relevance Propagation (LRP) and Backwards Optimization, emphasizing their potential for advancing machine learning applications beyond mere output accuracy. The paper advocates that the interpretability of neural networks should be valued as the ultimate scientific outcome, not merely a tool to ensure the outputs align with known principles.

Methodological Framework

The application of LRP and Backwards Optimization in this context is innovative. LRP traces the contribution of each input feature to a neural network's decision, effectively projecting the decision-making path onto the original input dimensions. This is particularly beneficial for case-by-case analysis, as it reveals which features are most significant for a network's prediction for individual samples. Backwards Optimization, on the other hand, iterates on input samples to maximize the confidence in achieving a specified output. This method synergizes with LRP by providing a composite view of the input patterns the network attributes to different output categories.

Applications and Implications

The paper provides two illustrative examples: ENSO phase identification and seasonal temperature predictability along the North American west coast based on preceding sea surface temperature patterns. Their neural network models, trained on historical data, achieved a high interpretation fidelity, revealing known climate patterns (e.g., the El Niño-Southern Oscillation) and identifying regions contributing to seasonal predictability within the Pacific Ocean.

Particularly notable is the ability of LRP to differentiate between the Eastern and Central Pacific phases of El Niño by focusing on relevant regions in each input sample. Such capability confirms the potential for discovering nuanced climate interactions and variability that are not easily discernible through traditional approaches.

Future Directions and Challenges

The outcomes of this research suggest fertile ground for future exploration. By leveraging the strengths of interpretable neural networks, geoscientists can refine their understanding of complex climatic relationships and even uncover previously unidentified patterns. However, the paper acknowledges the trade-offs between model complexity and interpretability. Crucially, neural networks need careful architectural considerations, balancing simplicity for interpretability with complexity for capturing intricate data relationships.

The insights from this research catalyze a shift in the deployment of neural networks within the geosciences—from opaque prediction tools to transparent, scientifically contributive systems. The methodologies highlighted could extend into other domains where understanding model reasoning is as critical as obtaining the results.