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 stream temperature as domains shift towards out-of-bounds conditions. This data release and model archive contains three zipped folders for 1) Data Preparation, 2) Modelling Code, and 3) Model Predictions. Instructions for running data preparation code and modelling code can be found in the README.md files in 01_Data_Prep and 02_Model_Code respectively.
|Title||Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin|
|Authors||Simon N Topp, Janet R Barclay, Jeremy A Diaz, Alexander Y. Sun, Xiaowei Jia, Dan Lu, Jeffrey M Sadler, Alison P Appling|
|Product Type||Data Release|
|Record Source||USGS Digital Object Identifier Catalog|
|USGS Organization||Integrated Information Dissemination Division|