GeoCryoAI: Ensemble Learning and the Permafrost Carbon Feedback in Alaska, 1963-2022

GeoCryoAI is a hybridized process-constrained ensemble learning framework consisting of stacked convolutionally layered long short-term memory-encoded recurrent neural networks. The purpose of GeoCryoAI is to quantify permafrost thaw dynamics and greenhouse gas emissions in Alaska.

The dataset includes pre-processed input data (i.e., thaw depth, active layer thickness, thaw subsidence; CO2 flux, CH4 flux) acquired from in situ measurements (e.g., CALM, GTNP, ITEX, SMALT STDM, ReSALT, AmeriFlux, NEON), remote sensing platforms (e.g., UAVSAR, AVIRIS-NG), and process-based modeling products. Field data were included to quantify CO2 and CH4 flux (e.g., chamber, eddy-covariance, and tall-tower measurements via flux tower networks) and active layer thickness (e.g., mechanical probing, borehole temperatures, ground-penetrating radar).