Case, Jonathan L.

Hain, Christopher R.

Schultz, Christopher J.

White, Andrew T.

White, Kristopher

NASA Technical Reports Server

 The burn area across the U.S. is not uniform and is generally split 
by east/west climate differences (Figure 4).  These spatial 
differences result in non-uniform relationships between 
antecedent conditions and fire potential.  
 Therefore, all datasets were transformed to standardized anomalies.  
 Due to the numerous amounts of available data related to fire 
potential, the use of a random forest (RF) machine learning 
algorithm to develop a burn area predictive model was explored. 
 Monthly averages of each dataset (Table 1) from the previous year 
through March of the current fire season were used as RF features.  
 The RF model was trained using 11 years worth of data (fire 
years: 2002 – 2012).  The model was then tested on 3 different 
years (fire years: 2013 – 2015).  An example RF tree in the model 
is shown in Figure 5.  
Exploring the use of Machine Learning to Develop a Predictive Model 
for Future Fire Seasons
Andrew T. White1, Christopher R. Hain2, Christopher J. Schultz2, Jonathan L. Case3, Kristopher D. White4
1UAH/NASA MSFC SPoRT, 2NASA MSFC SPoRT, 3ENSCO, Inc./NASA MSFC SPoRT, 4NWS/NASA MSFC SPoRT
Background
Wildfires in the United States can be extremely costly, 
both economically and through the loss of life.  The 
cost of fire suppression efforts alone surpassed $2B in 
2017, which is greater than 50% of the U.S. Forest 
Service’s total budget.  With high costs and a 
disproportional amount going to fire fighting efforts, 
predictive estimates of fire season severity (i.e. 
number of fires or acres burned) could be beneficial to 
management officials.  Fire severity is dependent on 
both the current and antecedent atmospheric and land 
surface conditions which is highly variable year to year.  
Partly in response to changing antecedent conditions, 
fire season variables such as number of fires and acres 
burned vary year to year as well (Figure 1).  
Antecedent Relationships
Figure 1: Yearly number of fires and acres burned across the CONUS 
domain.
 At the current state of development, the RF model shows some 
promising results (Figure 6). 
 While the predicted burn area does not exactly match the observed, 
the overall patterns are similar.  
 The model is only providing information on the yearly burn area 
potential based on the antecedent conditions alone.
 The current model does not account for different ignition 
sources.
 Human-related ignitions add uncertainty into the model as typical 
antecedent relationships in human ignition events are different than 
those in lightning initiated wildfires (Balch et al. 2017; Nagy et al. 
2018).  
 Since the number of fires started by humans is much larger than 
natural ignitions, optimal antecedent conditions alone might not 
explain the variation in acres burned from season to season.
 Ignition specific exploration with the RF algorithm has been 
planned.  While lightning started events are significantly less 
frequent, they contribute a large percentage to the area burned 
at ~56% (Balch et al. 2017).  
 The goal would be to produce probabilistic lightning wildfire potential.  
Methodology: Random Forest Regression
Figure 5: Example RF Tree
RF Features
SPoRT LIS Volumetric Soil 
Moisture 
(0 – 10 cm, 10 – 40 cm, 40 –
100 cm)
SPoRT LIS Soil Moisture 
Percentiles
(0 – 10 cm, 0 – 40 cm, 0 –
100 cm)
Dead Fuel Moisture (100-hr 
and 1000-hr) [Abatzoglou
2013]
Precipitation [Abatzoglou
2013]
Daily Minimum and 
Maximum Temperature 
[Abatzoglou 2013]
Daily Mean Vapor Pressure 
Deficit [Abatzoglou 2013]
Figure 4: Total acres burned (1992 – 2015) 
[Data from the 4th edition US Forest Service database (Short 2017)]
Table 1: Random forest features.
 Previous studies have indicated the importance of 
antecedent conditions on wildfires (Westerling et al. 2003; 
Crimmins and Comrie 2004; Morton et al. 2013; Nauslar et 
al. 2018).  
 Much of the focus has been on variables such as 
precipitation, temperature, and the Palmer Drought Severity 
Index (PDSI) while very little explicit focus on the actual soil 
moisture conditions.
 The SPoRT Land Information System (LIS) model data can be 
used to characterize the instantaneous and climatological 
soil moisture providing additional information on fuel 
conditions.  
Figure 2: Burn area standardized anomalies 
for 2011.  
High antecedent 
(2010) soil 
moisture during 
the growing 
season.
Low antecedent 
soil moisture 
during the late 
Fall 2010 into 
early Spring 
2011.
High 
standardized 
burn area 
anomaly 
over Texas.
Results/Future Work
Figure 3: SPoRT-LIS 0 – 40 cm monthly average soil moisture percentile  for 2010 (left) and 2011 (right).  
Figure 6: Observed acres burned (A) and predicted (B) for 
2015.
99th Annual American Meteorological Society (AMS) Conference on January 6 – 10, 2019, Phoenix, AZ 
Contact Information: andrew.t.white@nasa.gov 
A)
B)
https://ntrs.nasa.gov/search.jsp?R=20190000018 2019-08-30T04:57:57+00:00Z


Exploring the Use of Machine Learning to Develop a Predictive Model for Future Fire Seasons

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