Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics

Despite excellent real-world predictive performance of modern machine learning (ML) methods, many scientists hesitate to discard traditional physical-conceptual (PC) approaches due to their relative interpretability, which contributes to credibility during decision-making. In this context, a currently underexplored aspect of ML is how to develop minimally-optimal representations that can facilitate better insight regarding system functioning. Regardless of how this is achieved, parsimonious representations seem to better support the advancement of scientific understanding. Our own view is that ML-based modeling should be based in use of computational units that are fundamentally easy to interpret in a physical-conceptual sense. This paper continues our exploration of how ML can be exploited in the service of scientific investigation. We use the Mass-Conserving-Perceptron (MCP) as the fundamental computational unit in a generic network architecture to explore important issues related to the use of observational data for constructing models of dynamical systems. We show, in the context of lumped catchment modeling, that physical interpretability and predictive performance can both be achieved using a relatively parsimonious distributed-state multiple-flow-path network with context-dependent gating and information sharing across the nodes, suggesting that MCP-based modeling can play a significant role in application of ML to geoscientific investigation.