From cd237728e1154f0b0359a6104304aaa2a953961e Mon Sep 17 00:00:00 2001 From: Chaopeng Shen Date: Thu, 20 Aug 2020 14:29:07 -0400 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3f185bc..51041b1 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ This code contains deep learning code used to modeling hydrologic systems, from If you find our code to be useful, please cite the following 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 +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), https://doi.org/10.1029/2019WR026793 -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 +Fang, K., 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