https://fire-space-front-end-git-main-edison16as-projects.vercel.app/
At Los Altos, we built FlameSense, which helps first responders and the public by simulating wildfire spread using a sequential neural network trained on historical fire data and current weather conditions like temperature, humidity, and wind. Users can select locations on a map to see predicted fire growth visualized as a heat map, helping with better resource planning and awareness.
3rd Place
Predict Fires Data Collection Step 1: Data Collection We gathered current and historical wildfire and conditions data from various sources such as NASA FIRMS and Open Meteo. We collected factors such as humidity, temperature, dryness, and biomass. We then used Palantir's tools to clean and transform the data.
Data Cleaning Step 2: Training We then used the cleaned data to train a model in Palantir which would take in current data inputs such as humidity, temperature, dryness, and biomass and outputs a predicted fire radius.
Data Integration Step 3: Exposing Function We then converted the model into a typescript function. Then we exposed the function to be called through javascript.
Model Training Step 4: FrontEnd We built a front end in HTML which displays a map and animates how the fire will spread depending on a location.
Interactive Visualization Future Plans We plan to expand our model and train it with more data to make a more accurate prediction.