Neural Networks Workshop

Neural Networks for Snow-Water-Equivalent Estimation

Day 3 Session 1 (11:00 AM MDT)

Participants will explore the model using the Snow Water Equivalent Machine Learning (SWEML) model in a data-driven machine learning (ML) platform with a modular structure to account for the heterogeneity of climate and topographical influences on SWE across the western United States. The SWEML pipeline assimilates nearly 700 snow telemetry (SNOTEL) and California Data Exchange Center (CDEC) sites and combines with processed lidar-derived terrain features for the prediction of a 1 km x 1 km SWE inference in critical snowsheds. While the model consists of twenty-three regionally specific sub-models tailored to the unique topography and hydroclimate phenomena in the Western U.S., participants will select a single domain of interest (e.g. Sierra Nevada Mountains) to explore the ML pipeline and participate in data processing, model training, model evaluation, and figure development.