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Imagine being able to predict where a forest fire is going to happen before it happens.

While it might sound like the work of psychics, a group of Schulich School of Engineering students was recently challenged to use historical data to predict the risk of forest fires across New Brunswick.

The team — comprising Katrina Chanco, Nathan Darby, Layla Arab and Evan Krul — used their knowledge and skills to finish in first place in Programming at the annual , hosted by the University of New Brunswick.

“We were absolutely elated when our names came up as the first-place winners,” Chanco says. “Since the stakes were so high, it took a bit of time for it to sink in that we had actually won, but we are so incredibly proud to win.”

Disseminating big data

Chanco says the challenge was split into three stages.

The team was first given data for 73,000 fire zones across New Brunswick, with each zone representing one square kilometre.

Using that data, they had to design a mathematical equation to be used as a metric to evaluate the risk of a forest fire in each zone. This metric would then be used to compare the risks of fire in certain key areas like cities, towns and parks.

They were then given data on all fire stations across the province to design an algorithm to allocate each station to the key areas.

The hope would be to create a software program that could expand the work to all of Canada.

On-point presentation

Not only did the team have to come up with the programming for the project, they also had to present it to the judges, which meant needing to do things to stand out above their competitors.

“We built geographically accurate heat maps that aesthetically visualized our calculated-risk metrics and dangers posed to key locations within the province,” Krul says. “We had to have a well-planned and organized presentation, and we were able to respond to the technical questions effectively, and had an overall visible enthusiasm towards our craft.”

Admitting they were “totally drained” by the end of the competition, Krul says the team was proud of the work done and were simply hoping to finish in the top three.

Top of the class

This wasn’t the first time Darby has aimed for the top of the competition’s podium, having been part of .

He started thinking back to his first competitions at the ��ݮ����Ƶ����, in which few teams took part at the local level in hopes of qualifying for the national competition, and is amazed at how far things have come in such a short time:

It was incredible to see the change that started at the ��ݮ����Ƶ���� level in the number of teams and how competitive it was this year. The ��ݮ����Ƶ���� has now placed first in not only the west, but in all of Canada in Programming for the past two years, and it has been amazing to be a part of that.

He adds it’s evidence that Calgary is becoming Canada’s newest tech hub, something echoed by Arab, who calls it “the perfect epilogue” to her time at Schulich before she graduates in May.

 “It has been a long journey and I’m incredibly thankful to be seeing it through with my closest friends,” Arab says. “With ��ݮ����Ƶ���� winning the Programming competition at CEC twice in a row, I’m excited to see what’s next for the future of tech in Calgary.”

Calibre of talent

A total of five ��ݮ����Ƶ���� teams took part in the competition, which was held March 11 to 13.

In addition to the Programming team’s victory, Stephen Ng finished second in Engineering Communications while Kenzie Fjestad and Patrick Mitenko were given Judge’s Awards for Excellence in Innovation (Junior Design).

The students’ CEC success is being applauded by , BSc’08, BSC (Eng)’09, MSc’11, PhD’16, Schulich’s software engineering teaching chair and MEng software program director.

“I was incredibly excited when I heard of our teams’ success in the Canadian Engineering Competition,” he says. “These results clearly indicate the calibre of talent we have here at Schulich.”

As for the programming developed by Schulich’s winning team, Chanco says the hope is that it could be used to create software that could be used to predict forest fires across Canada.

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