DeepTEC

A deep learning laboratory for Total Electron Content prediction experiments, using Tensorflow 2.
Models can be found at ./models/. Some reusable layers are on ./models/custom_layers.py.
The c111 and c333 models were inspired by Boulch (2018).

BOULCH, A.; CHERRIER, N.; CASTAINGS, T. Ionospheric activity prediction using convolutional recurrent neural networks. arXiv:1810.13273 [cs], 6 nov. 2018. https://github.com/aboulch/tec_prediction/

The models on ./models/dev/gps_solutions.py were described on the article:
de Paulo MCM, Marques HA, Feitosa RQ, Ferreira MP (2023) New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast. GPS Solut 27(2):95. https://doi.org/10.1007/s10291-023-01442-4

download IONEX data

python3 downloader.py Or download from: https://drive.google.com/file/d/1Sm_PiVUIabaew_3Y7sT0NWBqu7xsdHvi/view?usp=share_link

create numpy representation for the data downloaded

python3 ionex_samples.py

Experiment configuration

The configuration file "config.csv" is used to setup many hyperparameters for each experiment, such as chosen model, input window, prediction window, train and test datasets, among others.

Batch processing

The columns "batch_train" and "batch_test" on "config.csv" can be used to perform batch testing. Set them as True on the line that describes the experiment and run

python3 batch_run.py

The results will be created on the "output" folder, under a subfolder with the experiment's name.

Training the network

python3 train.py

The "parameters.py" file is created during training. If you retrain the network, please remove it.

Evaluating the trained network (test)

python3 evaluate.py

Plot results

python3 plotresults.py

Google Colab

https://github.com/mauriciodev/tec_forecast/blob/main/examples/tec_forecast.ipynb