Search Results for author:
Found 6 papers, 0 papers with code
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter.
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes.
Entity Aware Modelling: A Survey
However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data.
Causal Modeling of Soil Processes for Improved Generalization
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.
Probabilistic Inverse Modeling: An Application in Hydrology
We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.
Approximate Bayesian Computation for Physical Inverse Modeling
Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data.
