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Developments related with the application of deep neural network models to FWI downscaling

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SantanderMetGroup/2023_Mirones_deepFWI

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Multi-Site Fire Danger Prediction Using a Spatially Coherent Convolutional Neural Network Model

Mirones et al., submitted to Geophys. Res. Lett., Jun.2024


CNN-MG scheme

Our study analyzes the ability of state-of-the-art CNN-based machine learning methods to model the multivariate spatial structure of the Fire Weather Index (FWI). Authors and corresponding ORCID can be found in the zenodo.json file.

2023_Mirones_FWI_ERL.ipynb is a Jupyter notebook based on the R languaje containing the code necessary to replicate our results.

environment.yml contains the versions of the python and R libraries employed to reproduce the results of the manuscript. A conda environment with the appropriate versions can be created by typing:

mamba env create -n deep-fwi --file environment.yml

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