Group 3 – Milestone4
Michael O'Neill, Stephen Hopkins, Bryant Sell
WILDFIRE RISK
Introduction
Wildfires are a real danger and need to be better predicted to know when and where
fires will occur. Weather has a big impact on not only the fire starting and spreading, but also
on the firefighters trying to fight them as well. For our project, we generated an algorithm that
uses multiple variables to compute a wildfire risk map. The objectives are to generate different
levels of risk utilizing threshold values for each variable, which we turned into probabilities to
show, on a map, where wildfire risks are the highest.
Data & Methods
For wildfires, we found data for days in which we knew that the fire risk was high in
parts of the country. The data is SREF ensemble model forecast. There are 21 model members
in the ensemble that compute similar, but different, results that are all possible. The final result
takes these ensembles and averages the answers from each member. The variables in the SREF
that we used include temperature, relative humidity, wind speed and precipitation. The only
SREF relative humidity variable is for 850mb which is not at the surface where we need the data
for. To derive something useful from this, we used the temperature dew point and heights at
850 mb along with the surface temperature to compute an estimate of the relative humidity at
the surface. For each of the five main variables, the risk assigns a ‘1’ for highest risk and ‘0’ for
lowest risk. However two of the categories are combined to give a total of four risks. The four
risks are then averaged together and multiplied by 100 at the end to give the percentage of
wildfire risk given by that particular model. To get the final risk, all 21 ensemble members are
added, and then divided by 21. To decide when wildfire risks are highest, we started with
precipitation. If there is no chance of precipitation at the location for the given time, the risk of
wildfire from precipitation is automatically set at ‘1’. If precipitation is expected, we divided it
into three categories. For precipitation during a 3 hour period of less than .05in, the risk is set at
‘1’, for precipitation between .05in and .5in, the risk is .5, and finally for anything above half an
inch, the risk is ‘0’. Next, we looked at relative humidity. For humidity of less than 25%, this risk
is assigned a ‘1’. For between 25% and 50% RH, the risk is ‘.5’, for RH between 50% to 75%, the
risk is given a value of ‘.25’ and for anything above 75% the risk is ‘0’. For the wind speed,
anything less than 5 mph the risk is automatically ‘0’. For wind speeds between 5 and 15 mph,
the risk is assigned ‘.25’, for 15 to 25 mph, the risk is ‘.5’, for 25 to 35, the risk is ‘.75’ and for
anything above 35-----, the risk is ‘1’. Finally, if the temperature is less than 32 degrees
Fahrenheit, the risk is 0. For temperatures above 70 degrees, the risk is ‘1’.
Results
The maps that are generated show areas where the probabilities are between 0% and
25%, 25% and 50%, 50% and 75%, 75% and 100%- each category designated by different colors.
To test the algorithm, we used four sets of mock data values for each variable and entered
them into the formula for risk calculation. This mock data are values such that we can verify
high and low risks. The risk calculation is simply the average of each category’s risk, for
instance we have a risk of 1 for precipitation and relative humidity, and a risk of 0 for
temperature and wind, and we should get out a total risk of 50%.
Discussion/Conclusion
This product can be used in a number of ways. The most obvious implication is for
firefighters who need to know when to be alert for potential wildfires. They may need to know
ahead of time when they will need to have extra resources prepared and ready for possible
fires. For those who live in fire prone areas, you need to know when you shouldn’t have open
fires outside or when you may need to be aware of fires nearby that may come close to their
homes. Disaster response teams may need to know where to set up relief efforts for those
affected if homes evacuated or lost. Fires also have a dangerous health concerns for those who
have breathing problems due to the smoke. In the future, our product could be fine-tuned if we
find that the variable requirements for the risk are off. We may need to add more categories or
change the percentage of risk assigned to each variable. More variables could be added to
make the model more complex. Lightning risk can be used to predict where fires may be
sparked. It could be implemented so that the used could type in a location and they would
receive the wildfire risk value for that particular location only. We could also make it easier to
change the data that is being used so that it can be updated for future forecasts.