How can we better prevent widespread forest fires?

In recent years, wildfires have spread and done billions of dollars in damage, to wildlife and civilization. Every year more fires burn across the United States, and recently Australia is experiencing a national crisis because of out of control bush fires.

The frequency of wildfires in the United States alone has increased by 400%, with more than 8.8 million acres burned in 2018. Even with existing systems and government aid, scientists expect by 2030 wildfires will have destroyed 55% of the amazon rain forest and other forests would increase between 25% and 53%.

Our fire prevention systems and management of the forests are not working.

What it does

Flare is a forest fire detection system for rangers and fire services. By harnessing the power of wireless sensor networks and data visualization we can help reduce wildfire risks.

The system is deployed by placing the forest health sensor network throughout the wilderness. Once placed, the sensors track an area's carbon dioxide, temperature, and volatile organic compounds. The system tracks these factors and alerts forest management and firefighting services when abnormalities are detected.

Higher than normal levels of any of the tracked variables will be shown with real time data visualizations that will provide ground level early fire detection and give responders time that they otherwise would not have by relying on the other existing visual based satellite and camera detection systems.

Flare is a web app to enable firefighters and forest rangers to use it both at base on computers and out in the field with mobile devices. By using this system, firefighters and forest rangers can react and deploy earlier and with a more informed strategy for faster fire containment.

How we built it

We used a mixture of physical computing, Firebase for database management, and React paired with Google Maps to create a dynamic web app.

By creating a mesh network of sensor nodes, data is gathered and sent to the Firebase database before being fed into the Google Maps API and visualized by React in the Flare web app.

Challenges we ran into

Our team ran into equipment challenges early on, having to improvise and adapt with limited physical computing parts to create a viable prototype. Rather than an Arduino Uno would want ESP32 chips with wireless capabilities. This would allow for easier data communication and transmission to the database server. Valuable time was spent finding work arounds with the available resources to produce the best results possible.

Specific issues that we encountered during development included receiving and pushing serial data from the Arduino Uno to the Firebase database by using python rather than a wireless connection from the hardware directly. Our developers had a difficult time with creating the react and Google Maps web app because of our novel approach that demanded a custom approach, documentation and guidance available were limited. Pulling data from the Firebase API to further customize our web app experience proved to be much more difficult than expected.

Accomplishments that we're proud of

Our entire team is very interested in the problem space and we are proud of the solution that we chose to create. Our developers worked tirelessly throughout the hackathon to create a working react prototype.

Most of all, our team is proud of the Flare solution that we have planned and created. The accuracy and cost efficiency are far above its would be competition if it was to enter the market today.

What we learned

The team worked through every problem that we faced, whether that be forging into the unknown without documentation to guide us, to learning new languages, like python, to overcome hardware and database limitations.

What's next for Flare

Our team worked together to create a 2020 road map plan that would guide us to launching our Flare solution by the end of the year. This would include acquiring and implementing the technology, before polishing the databases and web app. Raising funds for implementation and testing, reiterating design based on findings before finally launching.

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