Wang YH and HV Gupta (2023), A Mass-Conserving-Perceptron for Machine-Learning-Based Modeling of Geoscientific Systems, Water Resources Research
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR036461
We develop a physically interpretable computational unit, referred to as the Mass-Conserving-Perceptron (MCP). Networks of such units can be used to model the conservative nature of the input-state-output dynamics of mass flows in geoscientific systems, while Machine Learning (ML) technology can be used to learn the functional nature of the physical processes governing such system behaviors. Testing using data from the Leaf River Basin demonstrates the considerable functional expressivity (capacity) and interpretability of even a single-MCP-node-based model, while providing excellent predictive performance and the ability to conduct scientific hypothesis testing. The concept can easily be extended to facilitate ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems, thereby facilitating the development of synergistic physics-AI modeling approaches.