wild watch 🚒

a real-time wildfire analysis and prediction dashboard that helps monitor, analyze, and predict wildfire incidents in quebec using machine learning built for conuhacks-ix as part of the sap task.

tech stack ✨

• frontend: vite + react with tailwind css and framer motion for smooth animations
• backend: flask with scikit-learn for ml predictions
• data processing: pandas for data manipulation and analysis
• deployment: frontend hosted on vercel; backend deployed on render.

features 👾

• smart file upload: handles multiple csv files with validation and progress tracking
• resource management: tracks firefighting resources and optimizes their allocation
• incident analysis: comprehensive reporting of fire incidents with severity levels
• risk prediction: machine learning-based prediction of future fire risks using environmental data
• geospatial visualization: displays incident locations and resources on interactive maps using latitude and longitude coordinates
• cost analysis: detailed breakdown of operational and damage costs
• interactive dashboard: real-time visualization of fire incident data and predictions

💭 what I learned

• ml implementation: building and deploying a random forest classifier for fire risk prediction
• resource optimization: developing algorithms for efficient firefighting resource allocation
• data processing: handling and analyzing complex environmental and incident data
• google maps integration: implementing the google maps api to create interactive location-based features

limitations 🚨

• data dependencies: requires specific csv file formats for proper functionality
• resource constraints: fixed resource allocation limits for firefighting units
• model scope: predictions limited to available environmental parameters

improvements 🌱

• real-time updates: implement websocket for live data updates
• advanced ml models: explore deep learning models for improved predictions
• resource scheduling: add dynamic resource allocation optimization
• historical analysis: include trend analysis and seasonal pattern detection

.env file 📄

this project requires .env files for both the server and client, located in their respective folders. rename the .env.example file in each folder to .env, and update it with the necessary values. ensure these files are configured properly and not committed to version control.

running the project 🏁

to get the project up and running on your local machine, follow these steps:

• ensure python and node.js are installed.

1. clone the repository:

git clone https://github.com/barbaraeguche/wild-watch.git

2. navigate to the project directory:

cd wild-watch

3. run the backend:

   1. navigate to server directory:

   cd server

   2. install and activate virtual environment:

   python3 -m venv venv
   source ./venv/bin/activate
   pip install -r requirements.txt

   3. run the flask app:

   python3 app.py

   4. open http://127.0.0.1:5000 with your browser.

4. run the frontend:

   1. navigate to client directory:

   cd client

   2. install dependencies:

   pnpm install

   3. start the development server:

   pnpm run dev

   4. open http://localhost:5173/ with your browser.

preview 📸

wild-watch.mov