Stanislaus State Assistant Professor of Physics Wing To was having some beers with atmospheric scientists last year when they came up with an idea for a research project that could speed up predictions of the atmospheric effects of California wildfires.
The topic of the discussion was how to change predictive modeling to produce quicker forecasts of the atmospheric effects of a wildfire before the pollution spreads over a wide area.
The traditional approach requires the calculation of complex mathematical equations, but atmospheric scientists are now exploring faster modeling methods using data science instead.
This is especially relevant in California, with large forest fires happening more frequently, more historical data is available on how fires interact with the atmosphere. By using machine learning algorithms, also known as artificial intelligence (AI), scientists can use the data to model the effects of future fires.
By the time the group said their goodbyes, To had an idea for a research project to address the issue and commitments from the two atmospheric scientists to be the project’s expert advisers. He then secured two $5,000 Stan State grants, a Research, Scholarship and Creative Activity grant and an Innovate, Design, Excel and Asses for Success grant, to fund the project.
With the project now being researched, To’s project is collecting data from previous fire locations with the goal of using the data to upgrade simulation and modeling software that is already widely used by atmospheric scientists.
If successful, the upgraded software will be able to produce accurate predictive models within days instead of the year or more that the process usually takes. This could lead to better public health protection because faster modeling could provide accurate, advance warnings of wildfire pollution as it heads toward communities.
“The information about fuel that is often used is usually one or two years old. That is ok if you have a large wildfire in an area every 15 years, but nowadays because of climate change we are having wildfires in the same areas much more frequently,” To said. “We needed to find a way to update the fuel source map on a more regular basis.”
To chose the European Space Agency’s Sentinel-2 satellite, which flies over the Creek Fire site every 10 days to take new images, to frequently update their information. Then he hired five Stan State students from the physics and computer science programs to scrutinize the satellite’s multi-spectral images and painstakingly tag each pixel to identify the type of fuel.
One of the students working on the project is senior Taylor Whitney, who is a physics major. To recruited him to work on the project and he attended a conference where he learned about AI wildfire research.
“Using AI to process and classify data is a powerful tool, but preparing data for the supervised machine learning algorithms is an extremely tedious and painstaking task,” Whitney said. “We have to look through the spectral data, create false color images and classify each pixel, or region, as a different kind of fuel source before we feed it into a computer program. The hope is that if we do this enough the computer will learn how to automate the process of classifying fuel sources more quickly and accurately than current methods.”
To hopes this project will help scientists become more predictive when it comes to wildfires.
“When a fire happens, knowing where all the particulates are floating to, knowing where they will go and when they will get there within a day or two, that is pretty useful,” he said. “It is much more useful than knowing it a year later.”
