Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin

This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph convolution network - RGCN, and a temporal convolution graph model - Graph WaveNet). The associated manuscript explores how the architectural differences between the two models influence how they learn spatial and temporal relationships, and how those learned relationships influence a model's ability to accurately predict...

Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: DE, Delaware, Hydrology, MD, Maryland, All tags... NJ, NY, New Jersey, New York, PA, Pennsylvania, US, USGS Science Data Catalog (SDC), United States, Water Quality, Water Resources, climate change, domain shift, environment, explainable AI (XAI), generalization, hybrid modeling, inlandWaters, machine learning, modeling, process guided deep learning, stream temperature, temperature, water, water resources, Fewer tags