00:00This video is made possible in the description of the video.
00:30So here we present a new method to forecast the fire, danger. It uses machine learning
00:46method to combine a lot of information, not only weather but also fuel in terms of availability
00:55and dryness and most importantly source of ignitions. So where people live, road access
01:04to places. This is a substantial advancement on what was done before because previous method
01:12only used weather. So you see, I mean it's quite, it looks quite, much better than the
01:28classical FWI. And it's interesting that we've got the high risk actually of the desert regions
01:35where we don't have any fuel at all. And actually the real risk that we see in the machine learning
01:40model is in a different location. It's where actually a lot of fuel exists but there's
01:44also those weather conditions required for the fire. This is clearly not an information
01:51that is clearly not available for the standard fire weather index. Yeah, because you can see
01:56on the circles and triangles where we actually observe the fires. It's very much more in line
02:02with this model. This is really good prediction.
02:18So historically for fire forecasting we use what's called the fire weather index. And this
02:23is a simple physics based model where we use four weather variables, temperature, wind, precipitation
02:29and humidity to forecast the chance that if a fire does occur, how intense it will be.
02:36So what we know from that is that it doesn't account for a lot of things. It doesn't account
02:40for fuel, it doesn't account for ignition sources and things like that. So what we try to do
02:44here is we try to incorporate more data into a machine learning framework. So although a lot
02:49of this data can't be explained physically, we can use machine learning to get a better forecast
02:55way of using that data without the knowledge of the physics underlying it. So we incorporate
03:01fuel, ignition sources and also the existing weather that we previously used in the machine
03:06learning model to predict not just fire danger but the probability that fire might occur in
03:11a given location.
03:12I think machine learning has been a very, it can be a very effective way to merge together
03:18different type of information. Traditionally I mean physical models are able very well to
03:28Yeah, for example recent case in Los Angeles where fire really broke out in the wildland urban interface.
03:38This was really very severe because the previous seasons were actually characterized by very
03:45wet conditions which created an abundance of fuel that then was burned during the event.
03:55And of course this new method, the probability of fire, having the memory of the fuel abundance
04:03in their formulation allowed to really identify those regions that could be much more affected
04:10compared to simpler method they only consider weather. And this is why our prediction in this
04:16case was much more precise and pinpoint the exact location when very close to Los Angeles where
04:23fire really occurred.
04:24Yeah, yeah, so the purpose here is to try and produce a cost-effective piece of machine.
04:46that people can take away and use. So it's very expensive maybe to train but once we've trained
04:59the model here using our high processing computing power other centres can then take it and run
05:04it very cheaply and they can run it on their local laptops for example with very little cost.
05:09so I think the machine learning really really works.
05:38I think the machine learning really helps with getting the more precision on the location
05:42of where a fire might occur and this is relevant for agencies involved in suppression activities
05:47because then they can allocate resources into the locations where the fire might actually
05:52occur as opposed to just a widespread warning system that currently exists.
06:08In some ways it's a step change in fire forecasting because we're really going from suggesting where
06:28fire danger might happen, where there's the potential for fires to exist, to saying this
06:34is where we think fires will actually exist and for that reason it's quite a large leap
06:38forward I think in terms of fire forecasting.
07:08I think in terms of fire understanding, which is a form of fire being khiated further
07:10day to death I think in terms of fireструк begrudging looks radio
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