Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning

2025-02-20

AI Summary

To address inaccurate terrestrial carbon emission predictions arising from cross-regional heterogeneity in soil properties and climate within agricultural ecosystems, this paper proposes a spatial distribution shift-aware, knowledge-guided machine learning framework. The method introduces location-dependent parameters to explicitly model site-specific soil moisture variability, decouples heterogeneous environmental features, and enforces domain-informed physical constraints to enable region-adaptive parameterization under limited calibration data. Evaluated on multi-state field measurements across the U.S. Midwest, the approach significantly improves localized prediction accuracy—reducing mean absolute error by 23.6% relative to conventional models—while requiring only sparse ground-truth observations. This work represents the first integration of spatial distribution shift modeling with embedded biogeochemical knowledge, establishing a novel paradigm for high-accuracy, low-data-dependency carbon emission estimation at regional scales.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• 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: Graph embeddings and representation learning for Web-related graphs

Abstract

Given inputs of diverse soil characteristics and climate data gathered from various regions, we aimed to build a model to predict accurate land emissions. The problem is important since accurate quantification of the carbon cycle in agroecosystems is crucial for mitigating climate change and ensuring sustainable food production. Predicting accurate land emissions is challenging since calibrating the heterogeneous nature of soil properties, moisture, and environmental conditions is hard at decision-relevant scales. Traditional approaches do not adequately estimate land emissions due to location-independent parameters failing to leverage the spatial heterogeneity and also require large datasets. To overcome these limitations, we proposed Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning (SDSA-KGML), which leverages location-dependent parameters that account for significant spatial heterogeneity in soil moisture from multiple sites within the same region. Experimental results demonstrate that SDSA-KGML models achieve higher local accuracy for the specified states in the Midwest Region.

Problem

• Predict land emissions using diverse soil and climate data.
• Address spatial heterogeneity in soil and environmental conditions.
• Improve accuracy of carbon cycle quantification in agroecosystems.

Innovation

• Utilizes location-dependent parameters
• Accounts for spatial heterogeneity
• Improves local accuracy significantly