🔥 Forest Fire Predictor

An AI-powered web app that predicts and visualizes wildfire risk across Ontario based on user-uploaded weather data.

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🌍 What It Does

1. Upload a .csv file containing weather data for Ontario stations.
2. A machine learning model predicts wildfire occurrence tiers (Low / Moderate / High / Extreme) for the next month.
3. Results are visualized on an interactive Leaflet map with color-coded markers and a visible legend:
   • 🟢 Low Risk — Fewer than 50 fires — Routine local response
   • 🟡 Moderate — 50–150 fires — Regional resource prep
   • 🟠 High — 150–300 fires — Full deployment
   • 🔴 Extreme — Over 300 fires — National/international coordination
4. Each marker includes a GPT-generated explanation and prevention tip.

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🚀 Tech Stack

Tool/Library	Usage
🧠 scikit-learn	ML model (Random Forest Regressor)
⚙️ FastAPI (Python)	Backend API
🌐 React + Tailwind	Frontend web app
🗺️ React-Leaflet	Interactive maps
🤖 OpenAI GPT	Natural language fire risk summaries
☁️ Vercel + Render	Frontend and backend hosting

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🧠 How It Works

 ```mermaid graph TD A[User Uploads CSV] --> B[FastAPI Backend] B --> C[ML Model (.pkl)] C --> D[Predicted Fire Count] D --> E[Frontend Receives Prediction] E --> F[Animated Number Display] E --> G[Map Color Update] G --> H[CircleMarker Color & Tooltip] ``` 

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📁 Project Structure

forest-fire-predictor/
├── ml-model/              
│   ├── train_model.py
│   ├── merge_weather_fire.py
│   ├── forest_fire_merged.pkl
│   └── fire_counts_clean.csv
├── web-app/
│   ├── frontend/   # React + Tailwind + Leaflet
│   └── backend/    # FastAPI
│       ├── main.py
│       ├── model_loader.py
│       └── requirements.txt
├── README.md

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📦 Getting Started

🔧 Backend (FastAPI)

cd web-app/backend
pip install -r requirements.txt
uvicorn main:app --reload

🌐 Frontend (React)

cd web-app/frontend
npm install
npm run dev

Set .env in frontend/:

VITE_API_URL=http://localhost:8000

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📁 Sample CSV Format

Station,TempMax,TempMin,TempMean,Precipitation,CoolingDegreeDays
Pickle Lake,32,5,18,65,45
Red Lake,30,6,17,70,50
Sioux Lookout,33,4,19,80,60

Each row = 1 Ontario station 📈 Output = wildfire tier + natural language explanation

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🤖 Example GPT Explanation

“This location is at high risk due to elevated temperatures (34°C), low humidity, and low rainfall. Avoid open flames and clear dry brush near your property.”

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🗺️ Interactive Map Features

• Leaflet map renders all Ontario fire stations

• CircleMarkers color-coded by predicted fire count:

  • 🟢 0–49
  • 🟡 50–99
  • 🟠 100–149
  • 🔴 150+

• Hover tooltip shows station name + predicted fire count

• Legend explains color scale

• Smooth color fade transitions using CSS

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📈 Fire Prediction Model

Trained with a Random Forest Regressor to predict monthly fire counts from historical weather.

🔢 Features

Feature	Description
Tm	Mean Temperature (°C)
Tx	Max Temperature (°C)
Tn	Min Temperature (°C)
S	Rainfall (mm)
P	Precipitation (mm)
CDD	Cooling Degree Days
Month	Weather month number
Year	Year of record

🔍 Prediction Output

A fire count (rounded) → Mapped to Risk Tier:

Risk Tier	Fires per Month	Response Level
🟢 Low	0–49	Routine local response
🟡 Moderate	50–150	Pre-position regional resources
🟠 High	150–300	Full deployment
🔴 Extreme	Over 300	National/international assistance

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🧪 Sample Model Usage

import pandas as pd
import pickle

# Load model
with open("forest_fire_model_aggregated.pkl", "rb") as f:
    model = pickle.load(f)

# Predict
X_new = pd.DataFrame([{
    "Tm": 17.5,
    "Tx": 32.0,
    "Tn": 3.0,
    "S": 0.0,
    "P": 60.0,
    "CDD": 25.0,
    "Weather_Month_Num": 6,
    "Year": 2024
}])

y_pred = model.predict(X_new)
print("Predicted fires:", y_pred)

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✅ Current Status

✔️ Core pipeline complete:

• ✅ Cleaned weather + fire count datasets (2000–2023)
• ✅ Merged and aggregated per station per month
• ✅ Trained ML model on aggregated dataset
• ✅ Backend integration and prediction API
• ✅ CSV upload + file parsing working
• ✅ Map color visualization and tooltips

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⏳ UX Features

• Animated splash loader (~1.5s)
• Prediction button disables on click
• RingLoader spinner during model run
• Fire count animates from 0 to predicted value

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🔮 Future Enhancements

• 🔁 Integrate live weather APIs (e.g., OpenWeatherMap)
• 🛰️ Add satellite/vegetation data
• 🔄 Monthly model retraining
• 📊 Add dashboard for trend analysis

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📍 Live Demo

• Frontend: https://fire-predict.vercel.app](#)

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👥 Team

Member
Madison Zhang
Tenzing Woser
Preethi Kamalakkannan

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📜 License

MIT License