People can be in harms away very quickly. Decisions have to be
made in a relatively short period of time, and that's where
something like WIFIRE helps with managing those types of issues.
How about running a scenario on campus just as you come at five and then
running into the campus for six hours on a Santa Ana Day. WIFIRE
is a lab of scientists at UC San Diego, who have a goal
of making data and modeling tools available for wildfire awareness
and research. So, half an hour, one hour, one and a half, two hours, right?
The story begins in the year 2000,
when the High Performance Wireless Research and Education
Network began. It was founded by Hans-Werner Braun and Frank Vernon at
the San Diego Supercomputer Center. But, Calit2, now known
as the Qualcomm Institute, was also working with emergency
response agencies on simulating different types of scenarios
and integrating technology. So the director of Calit2 Larry
Smarr and the Chief Data Science officer at San Diego Supercomputer
Ilkay Altintas, put those pieces together. So, how you can
make the technology and the hardware and simulation and data science
integrated into a platform that can be seamlessly intuitive to
firefighters and the public. When you think about a location
and time that the fire starts, there is a number of data that
you can bring together. So, that one point is critical because on
a high wind day, that means this can take on in the next
half-hour, hour, six hours to be a large scale fire event.
If you could bring together all the data you can actually enable fast
decision support using models, data science, fire science, so that's
what WIFIRE achieved is that integration of data, models
scalable platforms, technology, together with people's expertise.
Field resources initially are very limited. We'll run a model
the initial model here from the Center and with the capability
of the program, we can put it in a shared file. I could take a
picture of the screen whatever means possible to be able to get
it to those initial decision makers that are out in the field
because they're making decisions on what's a realistic
containment area for this fire. If it all possible, where we
gonna be able to stop it. So, then how do you begin to start to
prioritize your structure protection? How do you begin to
prioritize your evacuation needs? How do you begin to
prioritize what communities you need to do alerts to and start
letting people know at one or two in the morning that, hey you got
a fire in your area. We need you to wake up and start paying attention to
some local media for some information. We built the WIFIRE
platform to be as intuitive as possible. So, you can turn on
weather stations and see which direction the wind is blowing
and how hot it is. You can see the different vegetation types
and you can see where the city is. And, you can also open up the
web cameras from HPWREN and Alert Wildfire and take a snapshot of
those cameras and see what the weather looks like. HPWREN is a
fixed wireless network that crosses San Diego County and
provides to the public, real-time weather and image information
of the County around them. HPWREN images are crucial to WIFIRE
mapping because it gives WiFi users the ability not only to
have a geographic overview of the fire perimeter, but to
actually see the fire perimeter. The weather data is preferred weather
data because the HPWREN meteorological stations update every
second and they have access to that per second updated weather data.
So, if the models aren't behaving properly because the
input data isn't right, we can update that with the new
information that we're getting from aircraft. A lot of the
software systems used for fire simulation today require a lot
of manual input. The way that WIFIRE works is automating a lot of
that so that you don't have to coordinate and specify a bunch
of data sets that are already there for you. That makes the
system faster, and can work for you. Red, orange, pink, yellow is
basically a prediction where the fire is going to go.
The process we use to predict where the fire's gonna go consists of
a joint combination of prediction and estimation, and some of the
estimation involves things that you cannot really directly
observe. So, a good example would be the burn rate of the fuel. How
fast is it burning? Those factors are part of the model, but
you don't know what they are, right? You cannot directly observe
them and so what we do is we take an observation and realize wow
the fire burned must faster than anticipated, so we can then learn
those adjustment factors so that next time our prediction is better.
People from all over California during the last couple
of years, every single big fire they come and find the system
and we have huge spikes in the use of our system. For
instance in fall 2017, we had more than eight million page hits
by eight hundred thousand people. And, when you think about it
sort of, it's a huge number 800 thousand people. "Who are
these people?" comes to mind, but these are really the public.
The people who have loved ones in the path of the fire or they
are in the path of the fire or in an evacuation center, and they are
trying to understand what's going on. The WIFIRE system provides
scalability. So when only a few people are running the model it
runs on just one machine and as we get more people running the
model it scales up to various clusters here and elsewhere in
the cloud. California had more than 6000 fires in 2018 and more
than 99 percent of these fires were suppressed so fire
suppression is actually pretty good. What's changing is the
conditions when these fires happen. We have stronger winds. We
have a big fuel buildup, which is the vegetation that burns. So
when a fire happens now, we are seeing all kinds of new behavior
So these 40, 50, sometimes even a handful of the "biggest fire ever," or
"this fire is uncontrollable" or "we don't understand this behavior" or
"we've never seen this behavior before" type of fires are
happening because there's something new going on and you can't
model those fires with the understanding that you bring about
previous or past fires. So we need to understand these fires
while they are happening so we can actually adjust the model. We
live in a what they call a wildland urban interface. That's where
we built our houses and some of those wildland areas actually
need fire to renew. So for the health of that ecosystem that we
build our houses on might need that fire to be burning. So,
over time with proper management and proper understanding of fire
behavior, we can probably manage these fires differently that
will be much healthier for our ecosystems and our communities as well.