Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon

• URL: http://arxiv.org/abs/2502.00672v2
• Date: Thu, 06 Feb 2025 18:41:16 GMT
• Title: Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon
• Authors: Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Z. Houlton, Ying Sun, Carla P. Gomes, Yiqi Luo,
• Abstract summary: We develop a Biogeochemistry-Informed Neural Network (BINN) to retrieve mechanistic knowledge from big data. BINN predicts six major processes regulating the soil carbon cycle from 25,925 observed SOC profiles across the US. The integration of neural networks and process-based models in BINN improves computational efficiency by more than 50 times over PRODA.
• Score: 20.23597761416224
• License:
• Abstract: Big data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding of the global carbon cycle and other biogeochemical processes. However, retrieving mechanistic knowledge from big data remains a challenge. Here, we develop a Biogeochemistry-Informed Neural Network (BINN) that seamlessly integrates a vectorized process-based soil carbon cycle model (i.e., Community Land Model version 5, CLM5) into a neural network (NN) structure to examine mechanisms governing soil organic carbon (SOC) storage from big data. BINN demonstrates high accuracy in retrieving biogeochemical parameter values from synthetic data in a parameter recovery experiment. We use BINN to predict six major processes regulating the soil carbon cycle (or components in process-based models) from 25,925 observed SOC profiles across the conterminous US and compared them with the same processes previously retrieved by a Bayesian inference-based PROcess-guided deep learning and DAta-driven modeling (PRODA) approach (Tao et al. 2020; 2023). The high agreement between the spatial patterns of the retrieved processes using the two approaches with an average correlation coefficient of 0.81 confirms BINN's ability in retrieving mechanistic knowledge from big data. Additionally, the integration of neural networks and process-based models in BINN improves computational efficiency by more than 50 times over PRODA. We conclude that BINN is a transformative tool that harnesses the power of both AI and process-based modeling, facilitating new scientific discoveries while improving interpretability and accuracy of Earth system models.

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