Toward emulating an explicit organic chemistry mechanism with a random forest model: dataset and training code

This repository contains the dataset created with the GECKO-A model and the code (training_gecko_rf_final.py) used to train and test random forests for predicting secondary organic aerosol formation.

For each simulation, results are distributed in two separate files identified as such:

_library__predictors.csv and _library__outcomes.csv.

is either ARO1 (toluene) or dodecane_4gen (dodecane).

is a unique simulation identifier.

the *predictors.csv files contain the state of the predictors for each timestep at the beginning of the chemical solver integration step.

the *outcomes.csv files contain the state of the outcomes at the end of the chemical solver integration step.

The TRAINING_* directories contain training simulations. TRAINING_ALL contains all the training data, used for the default random forest configuration. TRAINING_*NOX contain sorted training data matching LOW, MID and HIGH NOx initial regimes (see associated article) to train the specialized random forests.

Similarly, the VALIDATION_* directories contain validation simulations, used to test the random forests after training.

The TESTING* directories contain the results of testing the random forest for comparison with the VALIDATION simulations.