SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States

A working repository for code, plots, and notes related to a SML based model of western US wildfires. The manuscript outlining our model architecture and results is now available as a Geoscientific Model Development paper: https://gmd.copernicus.org/articles/16/3407/2023/gmd-16-3407-2023.html

Our study region with all fires between 1984 and 2020 is visualized with EPA Level III ecoregions below:

We predict the fire frequency and sizes in each grid cell using a Mixture Density Network (MDN) trained on climate, vegetation, and human predictors. Here's a schematic diagram of our machine learning framework:

The scripts may be executed in the following order:

1. ml_grid_plot_file.ipynb -- for reproducing all the plots in the preprint and Supplementary Information;
2. ml_grid_fire_frequency_file.ipynb -- for training the fire frequency MDN, calculating the SHAP values, and performing model cross-validation;
3. ml_grid_fire_size_file.ipynb -- same as above but for the fire size MDN;
4. prelim_data_processing_and_analysis.ipynb -- for pre-processing the raw fire and climate data; the data is then organized into pandas dataframes for training machine learning models.

Code requirements:

• python=3.7.11
• tensorflow=2.7.0
• tensorflow-probability=0.15.0
• scipy=1.7.0
• numpy=1.20.0
• xarray (version agnostic)
• pandas (version agnostic)

Use the package-list.txt file to set up a compatible conda environment for execution.