/street_canyon_models

Models accompanying the publicaton https://doi.org/10.1016/j.envpol.2020.114587

Primary LanguagePythonMIT LicenseMIT

Repository street_canyon_models

Models accompanying the publicaton doi: https://doi.org/10.1016/j.envpol.2020.114587

Applying Machine Learning Methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon

Iva Šimić, Mario Lovrić, Ranka Godec, Mark Kröll, Ivan Bešlić

Project structure

.
├── data                 # Data for modelling
├── results              # Results 
├── scripts              # Automated tests and run as .py
├── src                  # Source, models, tools, utilities
├── LICENSE
└── README.md

The code is set up as follows:

src has all the modules necessary for modelling

src/config.py has the configurations used by src/models.py src/preprocessing.py is used by src/models.py to preprocess the data partially src/feat_utils.py has some supporting functions and data scripts/run.py is the script for running the code

The original data can be downloaded from: https://zenodo.org/record/3694131

A preprocessed and imputed data files is present in the "data" folder

  • data/preprocessed.csv |

Running it

For running the script, a conda environment is recommended (or other Python distributions).

Conda installation: https://docs.conda.io/projects/conda/en/latest/user-guide/install/

Once Conda is installed the environment for running this script can be created as follows: conda create -n envpol python=3.6 scikit-learn=0.22 eli5 numpy pandas

In the scripts folder, the experiment python run.py