Hydrologic deep learning toolbox

This code, accessible here https://github.com/mhpi/hydroDL, contains deep learning code used to modeling hydrologic systems, from soil moisture to streamflow, from projection to forecast. The starting core of the code is a highly efficient LSTM code based on cudnn.

The work supported the publication of these papers:

Feng, DP, K. Fang and CP. Shen, [Enhancing streamflow forecast and extracting insights using continental-scale long-short term memory networks with data integration], Water Resources Reserach, (2020, Accepted) preprint: https://arxiv.org/abs/1912.08949

K. Fang, CP. Shen, D. Kifer and X. Yang, [Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network], Geophysical Research Letters, doi: 10.1002/2017GL075619, preprint accessible at: arXiv:1707.06611 (2017) https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017GL075619

Shen, CP., [A trans-disciplinary review of deep learning research and its relevance for water resources scientists], Water Resources Research. 54(11), 8558-8593, doi: 10.1029/2018WR022643 (2018) https://doi.org/10.1029/2018WR022643

Major code contributor: Kuai Fang (PhD., Penn State), and smaller contribution from Dapeng Feng (PhD Student, Penn State)