The ANN models are based on Tensorflow Keras framework
There are five directories, Main, BinnedModel, CrossVali, PostProcessing, and linear-multilinear
Main directory contains functions that are used in the paper. parm_signif_tests.py is to test parameter combinations and rank the models according to their predictive ability. Results are used to produce Fig.2a.
ANN_DMS_fine_tune.py is to fine tune the models. addAttribs.py is to convert coordinate and data/time parameters, and to add MLD normalized PAR. my_dms_model.py is to built a sequential ANN model.
BinnedModel directory contains functions to test models using binned data.
CrossVali directory contains functions to test n-fold cross validation models.
linear_multilear contains functions to test linear and multilinear models. credits go to Martí Galí at Earth Sciences Department, Barcelona Supercomputing Center, 08034 Barcelona, Spain for functions dms_sd02.m, dms_vs07.m, and dms_sat.m
PostProcessing contains functions to post-process the model results.
.csv and .mat data used to train the network can be found in the following repo.: https://zenodo.org/record/3833233#.XsM4cBP0nV4
author: Wei-Lei Wang on May 18, 2020.