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Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning

by Siyan Liu, Dan Lu, Daniel M Ricciuto, Anthony P Walker
Publication Type
Conference Paper
Book Title
2022 IEEE International Conference on Data Mining Workshops (ICDMW)
Publication Date
Page Numbers
1111 to 1119
Publisher Location
New Jersey, United States of America
Conference Name
Tenth Workshop on Data Mining in Earth System Science (DMESS 2022) In conjunction with the IEEE International Conference on Data Mining (ICDM 2022)
Conference Location
Orlando, Florida, United States of America
Conference Sponsor
Multiple sponsors
Conference Date
Terrestrial ecosystems play a central role in the global carbon cycle and affect climate change. However, our predictive understanding of these systems is still limited due to their complexity and uncertainty about how key drivers and their legacy effects influence carbon fluxes. Here, we propose an interpretable Long Short-Term Memory (iLSTM) network for predicting net ecosystem CO 2 exchange (NEE) and interpreting the influence on the NEE prediction from environmental drivers and their memory effects. We consider five drivers and apply the method to three forest sites in the United States. Besides performing the prediction in each site, we also conduct transfer learning by using the iLSTM model trained in one site to predict at other sites. Results show that the iLSTM model produces good NEE predictions for all three sites and, more importantly, it provides reasonable interpretations on the input driver's importance as well as their temporal importance on the NEE prediction. Additionally, the iLSTM model demonstrates good across-site transferability in terms of both prediction accuracy and interpretability. The transferability can improve the NEE prediction in unobserved forest sites, and the interpretability advances our predictive understanding and guides process-based model development.