A Bush Fire Prediction and Resource Deployment System

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  Google Maps Visualisation of Active Fire Spots

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  Elbow method for determining clusters in K-means

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  K-means on test data

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  K-means on training data

Inspiration

Bush fires have caused a lot of trouble in 2019, resulting in millions of hectares of forest burnt , lives lost (both animals and people). Therefore the team came up with a machine learning enabled way to help local authorities to better prepare for future crises.

What it does

It produces life saving insights to help save lives and property. This have been mentioned in Blackrock's environmental risk management strategy into the future where natural disasters could happen more frequently. We need to harness technology to help us to better manage these crises.

How we built it

We used Python and TensorFlow.

Challenges we ran into

Combining various machine learning techniques to gain insights is not easy. Finding suitable datasets is difficult.

Accomplishments that we're proud of

We managed to extract data from images to generate insights from climatic dependence of fire spread

What we learned

Teamwork skills and some machine learning skills.

What's next for Bush Fire Prediction and Resource Deployment

Built With

• python
• tensorflow