This code contains deep learning code used to modeling hydrologic systems, including soil moisture, streamflow and water quality, from projection to forecast.
K. Fang, M. Pan, CP. Shen, The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning, IEEE Transactions on Geoscience and Remote Sensing (2018) https://doi.org/10.1109/TGRS.2018.2872131
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 (2017) https://doi.org/10.1002/2017GL075619
K. Fang, D. Kifer, K. Lawson, CP. Shen, Evaluating the potential and challenges of an uncertainty quantification method for long short-term memory models for soil moisture predictions, submitted
I worked with Dr. Chaopeng Shen and MHPI group until the end of 2019. Please check this forked repo, where MHPI group is carrying on many interesting researches. Here are some papers from MHPI to read and cite:
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
Shen, CP., [A trans-disciplinary review of deep learning research and its relevance for water resources scientists], Water Resources Research (2018), https://doi.org/10.1029/2018WR022643
Two examples with sample data are wrapped up including
A demo for temporal test is here