Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
This addresses the challenge of accurate carbon cycle quantification in agroecosystems for climate change mitigation and sustainable food production, but it appears incremental as it builds on knowledge-guided methods with location-dependent parameters.
They tackled the problem of predicting land emissions by accounting for spatial heterogeneity in soil and climate data, achieving higher local accuracy for states in the Midwest Region.
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.