Inductive machine learning for improved estimation of catchment-scale snow water equivalent

Summary Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastructure, wireless sensor networks, and machine learning algorithms. Because snowpack distribution is highly nonlinear, we focus on Genetic Programming (GP), a nonlinear, white-box, inductive machine learning algorithm. Because we did not have access to near-real-time catchment-scale SWE data, we used available data as ground truth for machine learning in a set of experiments that are successive approximations of our goal of catchment-wide SWE estimation. First, we used a history of maritime snowpack data collected by manual snow courses. Second, we used distributed snow depth (HS) data collected automatically by wireless sensor networks. We compared the performance of GP against linear regression (LR), binary regression trees (BT), and a widely used basic method (BM) that naively assumes non-variable snowpack. In the first experiment set, GP and LR models predicted SWE with lower error than BM. In the second experiment set, GP had lower error than LR, but outperformed BT only when we applied a technique that specifically mitigated the possibility of over-fitting.