156 - Trigger Boundaries with Harry Mitchell and Nick Kalogeropoulos

This should have been a chance to show that everything went . Every layer of fire protection that exists went wrong in Mati and it was the time to assign responsibility when it should have been assigned , to show that there are so many more ways of fighting a fire short term , long term , whatever .

And with this trigger boundary methodology methodology we want to show that suppressing a fire is not the only option hello everybody , welcome to the fire science show .

In this episode we will be talking about wildfire preparedness and actually an engineering methodology that was developed in a project called Woonity , a very big project that we will be talking about and this part on imperial legend and under supervision of Guillermo Reyn .

This is called the trigger boundaries and it's a very nice way to assess when communities should evacuate , when they should leave , when is the last moment they can safely evacuate from a wildfire that is approaching them .

My guests today are two PhD students from Imperial Well , dr Harry Mitchell you've heard him before in the podcast on the smoldering of timber , and Harry's done a lot of work on trigger boundaries as well and my second guest , nick Algaropouloulos , who you've just heard seconds ago . He has a really powerful story behind him doing this job .

He has firsthand experience with wildfires . He's very charismatic and has done a lot of research to take the very first simple model they came up with into a probabilistic simulator .

So if you're hesitant to stay with us because this is wildfires and you don't do wildfires , oh boy , it's totally worth it , and I'm sure many of us will be doing wildfires in the future . Here we are given a tool that we can work with and , as engineers , we have to appreciate that . So let's spin the intro and jump into the episode . Episode .

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Hello , nik , good to have you in the podcast .

In the general theme of wildfire evacuation , I saw Nick's talk on IFSS and that was really , really interesting and I thought that the concept is quite brilliant and could be applied not just to wildfires but in many places like industrial fire safety or perhaps even in some way moved into the world of buildings , so I'm super happy to have listeners learn about it .

One of the things we found from , I think , professor Tom Kova , his team in the US was they used a methodology where they find an imaginary line around the community , to talk in fire safety terms , where R set equals A set . I'll explain for people that you know don't have a trying to approve document B .

So it's a line where the available time to evacuate , dictated by the progression of the fire , is equal to the required time to evacuate for the whole community . So when the fire reaches this imaginary line around the landscape , the population has an exact amount of time to evacuate before the fire reaches them .

If an evacuation happens before that line , then the entire community will have evacuated before the fire reaches them . If an evacuation starts after the fire has crossed this boundary , then there is a chance that people will still be in the community when the fire reaches them .

If an evacuation starts after the fire has crossed this boundary , then there is a chance that people will still be in the community when the fire reaches them . It's a sort of line to mark the last chance of safe evacuation .

We take that minimum parcel of land that has been decided on and , with the help of collaborators like Enrico from Lund , we estimate how long it will take for that community to be evacuated and do the analysis for that scope . That scale .

And you also know that if the fire is 10 kilometers from that line , you perhaps can use other strategies to mitigate or , you know , keep , because , like what people do when the fire comes , they would prepare their households or well-being for the incoming fire and then hopefully they would evacuate .

So perhaps you have more time to prepare or , in terms of industry , perhaps you don't need to shut down the factory . That was also an interesting discussion I just had with Eulalia Planas some weeks ago in the podcast . So a very simple but yet a very interesting concept . What does it mean that the fire crosses the line ? Like what exactly crosses the line ?

The flames , the smoke , the firebrands ?

So at the moment , just because it's what is available in terms of modeling capabilities , the trigger time itself is defined by the where the actual wildfire spread front reaches the trigger boundary . But I suppose there would need to be other considerations in reality as well , like when do firebrands pass the trigger boundary ?

When does smoke pass the trigger boundary as well ? Because , as we're seeing in the larger woundity project , smoke is quite a big consideration in the capacity of a community to evacuate . You know , if people driving through smoke where they can barely see in front of themselves , then that's seriously going to impede their capacity to evacuate .

So there are other considerations that need to come into it when we're getting into more complex models .

There's also the concept of is it safer for people to evacuate or is it safer to stay in place if there is a lot of smoke outside and if their building and community preparation is that that allows them to stay in place ? All right . Now we have some modeling to deal with smoke . It can be implemented in the three-year boundary methodology .

It's not implemented in the models we have so far .

It of course depends on what fuel might be burning , how the wind might be , if there's a lot of smoldering , because we've seen that the flaming part of the fire produces a lot of smoke but it's very hot and goes up , whereas the smoldering part of the water produces colder smoke , which is what ends up going into the communities .

So there's a lot of interconnectivity at play that can come into the methodology and the framework of trigger boundaries . But to select models that can do everything at once is a bit tricky .

Uh , how dry is the fuel ? How much the fuel is ? There must be so much , so much into it . We'll we'll try to dig into that . But first I need to also understand what's the point of doing this extremely complex modeling task . How do you see this , perhaps affecting the safety of communities ? Or what's the rationale behind developing this complex methodology ?

There will become a critical point where you might need to evacuate , but currently there aren't many resources or tools beyond firefighter intuition that define when is the time where we actually need to call an evacuation . So there's a lot of obviously there's a lot of research into wildfire spread modeling .

So semi-empirical models like Farsight and Flammat and Prometheus , and the list goes on and on and on . And then there's a mountain of research into evacuation modeling as well . But the interaction and how to couple and interpret both of those phenomena together is heavily understudied and undercharacterized .

So essentially , the point of trigger boundaries is to sort of bridge that gap , to try and understand okay , when do we need to evacuate people so that they don't end up evacuating into or towards the fire , or before , how do we evacuate them before fire starts to impede the evacuation

route ? Also ?

But I would love to understand , like , what could be the consequences ? Like how does it look when the evacuation is started , after those , let's say , trigger boundaries which were not known are crossed ?

Thankfully , not many people that we knew that were victims of the fire , but some people adjacent to us on the buildings opposite us did not make it . So we've seen the worst that can happen in a delayed evacuation .

The worst thing that can happen is that people might just not know what is going on In Matti , in that area , as I was like I grew up there Even since you , even since I was eight or nine I was used to seeing the sky turn orange , the firefighting helicopters coming down to the seaside to pick up water , so people were used to seeing smoke .

And when there's smoke over your area , maybe you don't believe that it's a fire that's headed to your place , and in the case of Malta , so many things went wrong that people realized that they were in danger when trees next to them started catching fire because of firebrands .

So the worst thing that can happen in delayed evacuation is that people don't know that they're in danger .

And if you don't know you're in danger , you haven't made evacuation plans , and the architecture of the community such that most buildings have wooden roofs , which means that you cannot make it a safe place to stay in place and avoid for it to pass , then your only option is to try and evacuate .

And if the road network is subpar and there is no coordination for evacuating cars , what can happen is a blockage , when either a car breaks down or there is too much traffic for the road network to handle , not the moment one person decides to abandon their car and go somewhere else . In the case of Mati , run to the beach . That's it .

The entire ingress route is blocked off , and in Mati there was only a north and south route . Anyway . That can happen . Some people decide to stay in their cars because the smoke is way too dense . Some people go to the beach , but the smoke is still way too dense . So , yeah , delaying an evacuation can lead to tragedy , and most often does .

We refer to such phenomena as dire evacuations , when people attempt to evacuate without enough time to do so safely , and we see it time and time again . We saw it in 2018 in Mati , in the Camp Fire , in 2023 in the La Jaina Fire in Portugal , in the Pedro Gao Fire , in individual cases , in the Black Saturday fires in Australia .

It happens time and time and time again . Circumstances are such that an evacuation either occurs or has to occur when there's not enough time to do so safely , either because of the strength of the winds , the dryness of the fuel or just the delayed process of starting the evacuation .

There has been a lot of research on that and even you know , I'm , with my limited experience with fire scientists visiting my , my laboratory , technically , people who are extremely well educated in in terms of fire sometimes seeing those you know , 10 megawatts fire out of nothing feels really scary and you've been to cold dreads and stuff .

You know how a big fire is . And even if you're ready for it , it's still surprising sometimes , and if you're a layman , especially if you've seen those images of orange sky , the orange sky will not be oranger because it's more dangerous .

Right , it's different things that indicate that you're in danger or not , and as a layman , there's absolutely no chance you can tell . There's no way out of your experience you'll be able to just say , oh yeah , this time it's really bad .

Perhaps in some indigenous communities where this knowledge was passed from generation to generation and they just lived in an area that has burned every three years , perhaps they've built a community intuition , but it's not something we should trust on when designing an evacuation process , right ? So I guess here the winity and furthermore , the trigger boundaries come in .

The first iteration of that was called the PERIL model . Am I correct , harry ?

along with a few of us at Imperial , lund , movement Strategies , nrc and RMIT . But basically the idea is that Peril could take one data from a wildfire spread scenario and an evacuation scenario and then develop a trigger boundary around the community from that . And also nice thing about it as well is that it doesn't necessarily have to be a model or it .

It can actually . Peril can actually be supported by data like actual experimental data .

So , for example , one part of the Woonity project was that our team ran an evacuation drill in a WooWee community called Roxburgh Park , essentially just asking I think it was , I can't remember maybe like 10% of the community to evacuate once evacuation notice was given , and from that we got an approximation of the required evacuation time .

So , for example , that data could be inputted into Peril and use that to support developing a trigger boundary .

You perform countless amounts of fire spread simulations and then fuel in and compare that with your evacuation simulation . How does it work .

So you take one case of okay , for example , we had a UK case study called Swinley Forest , which was a wildfire that happened I think in the early 2000s , which was modeled heavily by Tom Smith and the University of Greenwich , where basically they took burn data and then calibrated a wildfire spread model to that .

So you can take a single scenario and then do it like that or , as nick then developed into the next iteration of the model , you can take multiple evacuation scenarios or multiple wildfire spread scenarios and develop something that's a bit more generic , rather a bit more one-fits-all in terms of what a community could see .

Because , as we're getting sort of this , climate change is impacting things more and more . We are seeing more extreme wildfire spread scenarios , which will obviously make things much more complicated when it comes to evacuation .

Do you put any safety margin in that or you just say , okay , like 17 minutes is the trigger boundary and it's up to you to decide whether you like to evacuate 30 minutes or 10 minutes before .

After that the story goes I joined the Haze Lab and one of my first tasks was to take the work of Harry and try to integrate it into the wider Woonity project , university of Melbourne Movement Strategies , funded by NIST and managed by the National Fire Protection Association in the US , where we are the first to attempt a unified coupled wildfire evacuation model .

So the first platform , let's say that encompasses both wildfire simulations , pedestrian evacuation simulation and vehicular evacuation simulation , because when you have there's a lot of models that do the three independently , we're the first ones to try and do so in a unified content , and one of the great outcomes of such research is trigger boundaries .

So when I joined I was tasked with trying to do , you know , the nitty-gritty coding stuff to go from R development to Unity . And as I was doing it , enrico asked what would happen if I wanted to test out a fire coming , you know , both from the north starting there and from the south , starting there .

What if I want a boundary for those two fires and for a third fire that you know happened on a low moisture content day . That expanded to what if we test 10,000 fires ? By the end we realized that instead of studying single scenarios with a specific fire ignition point with quite a drastic evacuation , what if we try and do it probabilistically ?

The way I explained this in presentation is imagine if we had to evacuate this room right now . I could time you and take a very specific amount of time to evacuate this left theater . But what if I needed to know how specific amount of time to evaluate this lecture ?

But what if I needed to know how long it will take to evaluate this room tomorrow or with another class or in a year from now ? At that point you can't have a precise number , but what you can do is you can have a range of possible numbers .

It might be slow , it might be fast , it might be a slow day after lunch , it might be a day where it's only first year undergraduates , so they're very speedy to go , so on . We took trigger boundaries to the same direction , so we can model a single fire and a single evacuation .

But what if we want to study what will happen on any possible fire that might threaten this community for any realistic evacuation time . What we're doing now is we are stimulating single fires again , yes , but we're doing so in an assembled fashion as you described . We're doing hundreds of simulations until convergence is achieved . What does that mean ?

I'll leave it as a question mark .

But until we're satisfied with the result , we run individual peri-studies and bundle them all together in the end , by the end , instead of the fictional line that we were talking about before , where it evaluated before the parity line , you say if after there's a bit of a problem , now we have this sort of band , I'd say , around the community , by that evaluation ,

completely outside of the band , you have a 100% chance of evacuating on time . 100% chance when it comes to the model input parameter you selected . And as you delay your evacuation and the fire progresses towards the community and into this band , your probability of evacuating safely decreases .

You might delay your evacuation by a bit and you might decrease it so that now you have an 80% chance of evacuating safely decreases . You might delay your evacuation by a bit and you might decrease it so that now you have an 80% chance of evacuating safely . You might delay it more and now you have a 40% chance of evacuating safely .

This percentage the way we do it now comes from doing repeat simulations based on the historic wind . So we would look at the weather station data for the last 20 years , find what's the average daily maximum wind , that's the average daily minimum humidity , and how does that vary within a month ?

But it's not just wind , it's all those parameters that dictate fire behavior bundled together .

very small probability , because the probability of each individual wind is very low . There's like 2% of Western wind with five meters per second . These are very low probabilities , right , but the fun part about wind is that , even though it's a collection of low probability events like each wind has its very low probability , you always have one .

So , like the probability you will have one of them is 100 percent , because that's the windrows , that's all the historical wind , very , very interesting . One more thing that I need to clear so you said it was the first coupled approach Is the coupling bi-directional ? Does it mean that your fire simulation influences the evacuation simulation ?

Do people move differently in different fire scenarios ? Like you block roads and stuff like that ?

That will affect the evacuation speed . There is no difference from the evacuation model to the fire model .

I don't know how that happened , but one important thing to mention is that as of now we can but do not model firefighter intervention , because we want to model the worst case scenario , the worst case conditions , because we know that the fire will spread but we do not know that firefighting actually will happen like in Máté .

Does the model show that Exactly that ?

If we produce a map centered with a community and draw a line or a boundary around it , we can then use that as evidence-based decision making and say that if you have a fire coming from the west , your trigger boundary extends so far out because your main means of egress might be compromised , because it is dry shrubbery , which means that the fire is going to

come in extremely quickly . Now you have a visual indication of your weakest points of entry from the fire and you can have a discussion with the community , with managers , with landscapers , of we need to do something about this direction , because this is a very high fire and truth direction . This is something that we take pride in .

The model that we now have , we have produced a way of having numbers and evidence-based decision-making for community resilience .

But seeing the amount of development and interest in this area and knowing that there are a lot of tools to , for example , take the real-time wind data and put them into wildfire models , you perhaps could run it on the side to take decisions . Is this also something you have expected out of this software or it's just fully preparedness ?

So you know , if you're in in a rush to develop a wildfire model and get your fuel moisture content massively off , then that's going to have implications for your wildfire spread data and likewise with the evacuation .

So I think it's something that could be used immediately as a um like , for example , for common operating pictures while a wildfire scenario is happening . But I do like the other option as well of being able to use it as a more long-term community planning tool , and that allows a bit more fine-tuning and playing with the model to try and work

out okay . For example , what would happen if we , like carried out some prescribed burns on this area of wild land ? How would that affect your trigger boundary ? How about if we added a road here ? How would that affect the evacuation time ? And how would that affect your trigger boundary ? How about if we added a road here ?

How would that affect the evacuation time ? How would that alter the trigger boundary ? So I think , long story short , there's a lot of ways to boil an egg .

Of course , the added value is having an informed number on the evacuation time . Let's talk about the modeling , because that's also very interesting for me . The modeling , because that's also very interesting for me . So , when you do those models that feed the trigger boundary analysis for a community , what goes into such a model ?

You've already mentioned historical wind , but perhaps you can tell me a little bit more about how you define them and perhaps what fire model you use .

The first case studies that we used for KEREL and KPEREL was the test community evacuation of Roxborough in the US the test community evacuation of Roxborough in the US . After that we learned how it works . We learned its capabilities , assumptions and limitations and we appreciate the fact that it is generalizable in that they provide some ready-made fuel models .

In this case fuel models , they bunch up all the variables that affect fire spread depending on the vegetation . Sorry to interrupt you , but how does it work ?

Let's call it a very physical model , in that most of it background mathematics relies on physical knowledge , be it Navier-Stokes equation , diffusion equations , mass conservation equations . They are very accurate but take a very long time to produce a result . You know you can wait for a week for an FDS study . We have fidelity .

In our case , in Wildfires we have such programs Like FDS has a subroutine , subzoner , submodel called WFDS for Wildfire . But we want models that can run in the minutes .

We want operational models , ones that we can either run now and get a result in five minutes , or one that we can run hundreds of and get a result in five minutes , or one that we can run hundreds of and get a probabilistic outcome within the hour . So the way that is usually done is either empirically or semi-empirically .

So either you use slide rules and linear relations and regressions to know that , to find a relationship between how fast the wind is blowing and how fast your fire will be , or you start from a physical basis , say , for example , studying how heat transfer between cylinders might occur and build that up to a model where you might find that at some point you need

a constant that you have to find experimentally . Farsight does the second part . It is based on a mathematical principle called the Rothenberg model . It does the second part .

It is based on a mathematical principle called the Rotherman model , which essentially its foundation is that there's going to be so many listeners that you know there's going to be Mark listening . That has made this model I'm going to be explaining .

Like de Rochemaier was a very smart man , along with Andersen and Albini and anyone else I'm forgetting from the Missoula Fire Lab , and you know they extrapolated and said from this main equation how do we find , for example , how much energy a unit of fuel puts out ?

And they do the math , extrapolate , and they come up and say that , oh , the value that dictates this is the surface area to volume ratio , for example . You can find that experiment , you can define it , or it comes down to the bulk fuel moisture .

You can use FDS to model , or you can do a few experiments , see how the fuel moisture varies with time and so on . So this is what the model does .

You give it these bulk values that represent the fuel moisture , the topography , the wind , the fuel structure , and in it it has some empirical relations , meaning Excel , linear regressions of how each variable affects the fire itself , plugs them all together and tells you how fast the fire will spread at any given point . So the output is flame spreads velocity .

Let's say level one output would be the speed of the fire , its rate of spread . One output would be the speed of the fire , its rate of spread . The level two output would be starting from some sort of ignition , how might it spread over time ? And at that point you get to not in Firesight but in general you get to choose .

What methodology do I want to use ? It might be , as you pointed out , cellular automata where you sort of have a rule set of spread , like maybe a cell is , maybe a point in space , is burning for 10 minutes and then spreads to its neighbor , or something like that .

It applies a rule for how it's going to spread , it applies that rule , and then you have the next 15 minutes and this basically provides you with the location of your fire front Exactly and how you decide when it is too late to evacuate .

Or you just simply look at the time , you simply look at the spread time of evacuation and when this is reached , that's it .

These models have not been developed to deal with fire entering a community . We have been dealt with large fires out in the forests of I don't know , idaho , I don't even know if that has forest . But that means that we are very reserved in modeling the fire reaching and entering a community , in modeling the fire reaching and entering a community .

So , both for limitations in what we can do and for optimizing for safety , we say that the fire has entered the community and it's now the ultimate time .

When the fire reaches the first boundary , the first , let's say , houses that border the forest , we do not model the fire going into the community , spreading through it , because we cannot do that with any degree that we know is accurate . So with that in mind , we say that let's say time is zero .

When the fire reaches the first houses of the community , we go back and see where the fire is , let's say , 120 minutes before , because we know that our community takes 120 minutes to evacuate . That's when we know if it's too early or too late .

If you know how long the fire will take to reach you , then that's how you can determine if it's too early or too late and that's why you need fire modeling .

With that you can backtrack , which is what we did in Peril and JPeril and you can know that , with the fire approaching , I have this much time left before it reaches the first house that borders the forest .

What have you learned from applying this tool ?

But the next step in order to validate or at least benchmark them , is to conduct a evacuation case study , like we did at roxburgh , where you have a hypothetical fire line approaching a community and a trigger boundary that you've developed using peril or k peril or whichever trigger boundary model of your choice you want , and then evacuating the community at that

point . So I think the next step in terms of practical implementation because fundamentally it's a very visual and very practical coupling method and we'd want it to be able to be used along with the Woonity engine in a practical sense for communities , rather than just this tool package that just sits on a server completely unused .

Because we want to benefit communities , we want to make them safer .

This time , this paper is , I'd say , a personal achievement of mine , because we apply the probabilistic trigger boundary methodology to the Matic community with the pretense of doing this before the 2018 fire . If you were to do this trigger boundary study for 2018 , what could you have predicted ?

How could have this been used to avoid the tragedy , and what does it teach us about the tragedy itself ?

Without spoiling much of the paper and this is something that , again , I can talk for hours but the primary outcome of the paper for me not maybe not so much for science , but for me as someone that has to live with the consequences of the mighty fire is that , even upon fire ignition , if the whole of the community at once decided to evacuate when the fire

started , there was a 34% chance of tragedy .

And I say this because the legal trial regarding the Mati fire has just ended , maybe a month ago , and it resulted in six sentences one for the person that started the fire , who's going away for 500,000 years probably , and the other people that were sentenced as doing something wrong on the fire were six officers of the fire service .

I think one of them was the aerial means supervisor , who should have moved the firefighting airplane from one airfield to the other , or a few other officers . If they did something wrong , they need to be punished for it .

But the fact that only people from the fire service were sentenced is a message to the Greek community , both the firefighting community and the general public , that the only means of fighting a fire we have available is firefighting .

With these results and with three-year boundaries in general , we want to show that no , when a fire starts with 34% chance of a dire evacuation 34% chance of tragedy . You cannot just rely on fire suppression . That age has passed . You need to look at so much more . You need to look at community resilience , you need to look at the fuel condition .

How did the fire spread at a peak rate of spread of 130 meters a minute ? This should have been a chance to show that everything went .

Every layer of fire protection that exists went wrong in Mati , and it was the time to assign responsibility , when it should have been assigned , to show that there are so many more ways of fighting a fire short-term , long-term , whatever . And with this trigger boundary methodology , we want to show that suppressing a fire is not the only option .

And if we maintain this mentality of the only thing we can do is wait for a fire to start and then send people to fight it , it's not enough anymore . Fires are so strong , people are moving back to rural communities . You need a new approach to this . And now you know we're not just saying it because we have studied or something . We know .

We're saying this because we have the numbers . We can put them on a map and show you that this approach is not enough . You start off with a realistic chance of death . You need to start doing something about it . That's not , you know , putting another 20 million into getting a helicopter , which you should , but you should also consider .

Maybe start to try and prescribe fires , maybe start doing fuel conditioning , maybe start planning evacuation . Maybe start fire resilient landscaping , maybe start invading wall breaks .

You could fund the fire science until the sun blows up with this amount of money Seriously , and we would be very happy if you did .

Sun blows up with this amount of money Seriously , like , and we would be very happy if you did , and , on the other hand , you would have I'm not sure which country , but I'm very sure I've read the news that some countries , like gonna spend $2 billion for new airplanes , you know , strengthened their aerial firefighting team . Wow , that's that I mean .

That's great , that's a strategy . But if , out of those $2 billion , you could perhaps use 20 million to carry out analysis for the 100 communities that are perhaps at the biggest risk and , based on that , prepare some informed decisions on how to approach the evacuation process from those communities , it perhaps saves so many more lives with that money .

I often say that everyone wants to invest in safety and no one wants to . You know , save on safety that's what we often hear . Oh , we cannot save on safety . We need to invest in safety , but investing in a stupid way is not really an investment . That's a burnt down money .

We want to see an integrated fire management plan happening where people dealing with suppression get new equipment , get new training , people dealing with detection have the means they need , people dealing with evacuation can do plans and actions and academia on the side .

I think our job is to come up with the new , promising , exciting algorithms and plans and frameworks and structure and data that can be used in fire safety . So you asked earlier can you use Perry during a fire to find the three-year boundary coming ?

Right now you can , and we hope that every wild and urban interface community has a three 3G boundary surrounding it at some point . Our job is to provide you with this idea and say that we have done the research . This is a very good idea . This is a promising framework . This is a methodology that works to save lives .

We want to think about it , produce it and then give it out to the world to say this is a good idea . You should adopt this idea . You should see if you can use it in your community , and then people can say you know , I will use it with . I'll use it with a Canadian model , I use it with the sumo evacuation algorithm . I use it with WFDS .

If I want to use it with the WRFS fire , you can do that . We will be glad for you

too . We want to keep innovating in what the framework can do and what the framework looks like , more than we can use this fancier tool to use it .

Not not necessarily everyone's cup of tea , uh , but the overall sort of broader objective for trigger boundaries is the integration of k peril into the woundity engine . So essentially that means that people don't necessarily have to . You know , take a python script for trigger boundaries and then their wildfire model and their evacuation model .

We're aiming more as a part of the large-ability collaboration to have a tailored tool where people can have an evacuation model and a wildfire model already integrated and a trigger boundary model at that interface , which makes it , I think , a lot more readily usable for stakeholders .

So it has topography and fuel data for the entire northern US Then can equally input conservation data for a small plot of land , like we did for a few other case studies . A few other case studies , or even , as I did in the very early peril paper , just generate , like some , completely artificial . You know what happens if we have flat land , uniform fuel .

How does the trigger boundary form in that kind of condition ?

Fantastic guys .

That's dreadful . Anyway , I haven't seen that then . I've seen , you know , fts . I've seen smoke control . I've seen optimizing detection times . I've seen simple evacuation of buildings and that was the market for me to do my safety engineering . You know , to make this as a way of living .

And today , looking at the tools that you are developing , I see communities who would like to invest money in professional studies that would exactly give them a nice report with a map that would show here's your worst scenarios , and perhaps you should investigate in that and based on my experience so far , there is also money in civil preparedness .

If you can convince people that this is a given benefit , there's money in that . I'm not suggesting that it should become a money cow , but it could become a very important part of fire safety engineering where people do professionally these types of analysis and when they do , I hope they listen to this fire science show episode and send 10% your way .

The goal of Hazelab is community safety .

So I think this this is a fantastic way , and whenever you guys end up after your academic or Hazelab episodes , well , hopefully , you can stay in Hazelab as long as you can , but I hope you will be continuing improving this and building up , because I think this is something that fire safety does really need .

If any of the audience was questioning the brilliance of Guillermo in any of your episodes , the way all of this happened if you go back to the story , I told you how Enrico asked us to look what happens if two parts happen at the same time .

How everything started is that I took my results to Guillermo , where I had two trigger boundaries and a weird number that started coming up , which was how many times each point on the landscape was in a boundary . Guillermo just said try and keep track of that number . Let's see what happens if you just keep track of that number .

And two years later , this number is , you know , the percent chance of dire evacuation . None of this would have been possible without Guillermo . So we thank you very much on this podcast as well .

Perhaps they'll agree to do the evacuation counterpart . That would be very interesting to close the loop At least so .

When I entered the profession , I only thought I will be doing buildings , but I clearly see myself doing community assessments right now , and there are thousands and thousands of communities that could actually benefit from analysis , like the ones that they have prepared for their case studies . So great job , guys . Continue your project .

I'll try to get Enrica and Erika to talk about the evacuation counterpart . I also have a feeling this will be very , very interesting . Actually , I had them both in the podcast separately . Erika was talking about wildfire evacuation , but that was many years ago and I wonder what changed in that regard .

Anyways , I'm super happy with this episode and I hope you are as well . Thanks for being here with me and guess what ? See you here next Wednesday . It's gonna be another great one . Cheers Bye .