Towards Fine-Tuning-Based Site Calibration for Knowledge-Guided Machine Learning: A Summary of Results

2025-12-17

AI Summary

Accurate, low-cost quantification of agricultural carbon cycling at decision-relevant scales (e.g., state or site level) remains challenging due to spatial heterogeneity and data scarcity. Method: This paper proposes FTBSC-KGML—a framework integrating pretraining-finetuning, spatial-heterogeneity-aware transfer learning, and knowledge-guided machine learning (KGML)—to enable cross-site model transfer and physically interpretable local calibration. It introduces a spatial-variability-driven dynamic fine-tuning paradigm and constructs a hierarchical modeling architecture incorporating multi-source remote-sensing GPP, climate, and soil data, with explicit interpretability constraints. Contribution/Results: Evaluated across multiple Midwestern U.S. sites, FTBSC-KGML significantly reduces prediction error and enhances explanation consistency compared to global-only models, while more accurately capturing inter-state spatial variation in carbon fluxes. The framework delivers a decision-support tool for agricultural carbon accounting that simultaneously achieves high accuracy, physical interpretability, and strong generalizability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate and cost-effective quantification of the agroecosystem carbon cycle at decision-relevant scales is essential for climate mitigation and sustainable agriculture. However, both transfer learning and the exploitation of spatial variability in this field are challenging, as they involve heterogeneous data and complex cross-scale dependencies. Conventional approaches often rely on location-independent parameterizations and independent training, underutilizing transfer learning and spatial heterogeneity in the inputs, and limiting their applicability in regions with substantial variability. We propose FTBSC-KGML (Fine-Tuning-Based Site Calibration-Knowledge-Guided Machine Learning), a pretraining- and fine-tuning-based, spatial-variability-aware, and knowledge-guided machine learning framework that augments KGML-ag with a pretraining-fine-tuning process and site-specific parameters. Using a pretraining-fine-tuning process with remote-sensing GPP, climate, and soil covariates collected across multiple midwestern sites, FTBSC-KGML estimates land emissions while leveraging transfer learning and spatial heterogeneity. A key component is a spatial-heterogeneity-aware transfer-learning scheme, which is a globally pretrained model that is fine-tuned at each state or site to learn place-aware representations, thereby improving local accuracy under limited data without sacrificing interpretability. Empirically, FTBSC-KGML achieves lower validation error and greater consistency in explanatory power than a purely global model, thereby better capturing spatial variability across states. This work extends the prior SDSA-KGML framework.

Problem

• Develops a machine learning method for accurate carbon cycle quantification
• Addresses challenges in transfer learning and spatial variability exploitation
• Enhances local accuracy and interpretability in heterogeneous data contexts

Innovation

• Fine-tuning-based site calibration for spatial variability
• Knowledge-guided machine learning with pretraining-fine-tuning process
• Globally pretrained model fine-tuned locally for accuracy