/AT-GRU

Predictive deep learning model for subsurface ocean chlorophyll concentration.

Primary LanguagePython

AT-GRU

Predictive deep learning model for subsurface ocean chlorophyll concentration. This code is from the paper ''Sparse2Dense Reconstructing Chlorophyll Profiles with Deep Neural Network to Predict Global SCM''. Match_SST.py and Match_Chla.py are code examples for spatiotemporal matching of sea surface temperature and sea surface chlorophyll concentration, respectively. The entire code consists of three parts. "model.py" describes the network structure, "preprocess.py" includes the preprocessing and partitioning of data, and "train.py" describes the entire training process.