Hardik Mahipal Surana Pranshu Dave Tanvi Pisat
- Setup a virtual environment
virtualenv -p python3 venv
source venv/bin/activate- Install project dependencies
pip3 install -r requirements.txt- Run regression models
python src/regression_models.py data/combined.csv| Column Name | Description |
|---|---|
| FOD_ID | global unique identifier |
| FIRE_NAME | name of the incident |
| DISCOVERY_DATE | date when the fire was discovered/confirmed to exist |
| CONT_DATE | date when the fire was declared contained/controlled |
| STAT_CAUSE_DESCR | Description of the (statistical) cause of the fire |
| FIRE_SIZE | Estimate of acres within the final perimeter of the fire |
| FIRE_SIZE_CLASS | Code for fire size based on the number of acres burned |
| LATITUDE | in decimal degrees |
| LONGITUDE | in decimal degrees |
| STATE | Two-letter code where fire occured |
| distance | distance of sensors from fire |
| date_time | when the data was collected |
| air_temperature | air temperature (1 day reading) |
| relative_humidity | relative humidity (1 day reading) |
| wind_speed | wind speed (1 day reading) |
| precipitation | precipitation (1 day reading) |
Air Quality Air Quality Class/Category Air pressure
FFMC index DMC index DC index ISI index Buildup index
vegetation fuel
altitude/height above sea level
- Heteroscedasticity - When dependent variable's variability is not equal across values of an independent variable
- Multicollinearity - When the independent variables are highly correlated to each other
- Underfitting - high bias
- Overfitting - high variance
- date columns in the database are in julian days
- FIRE_SIZE_CLASS - A=greater than 0 but less than or equal to 0.25 acres, B=0.26-9.9 acres, C=10.0-99.9 acres, D=100-299 acres, E=300 to 999 acres, F=1000 to 4999 acres, G=5000+ acres
- Kaggle
- AirNow API
- Mesowest API