EngrIBGIT/DataSurge_FloodPrediction

# FloodGuardEdge **AI-powered Flood Prediction App for Nigeria** Combines geospatial intelligence, machine learning, and real-time weather to help predict urban flood risks. # FloodPrediction ## FloodGuardEdge **AI-powered Flood Prediction App for Nigeria** Combines geospatial intelligence, machine learning, and real-time weather to help predict urban flood risks. --- ## Features - **Satellite Data Integration** (CHIRPS, SRTM, MODIS) - **ETL, EDA & ML modeling** using Python & SHAP - **ML Models**: RF, XGBoost, LightGBM, ANN, etc. - **Weather Forecast** integration with OpenWeatherMap API - **Nigeria Map Tab** with real-time data and scrolling info - **Offline support** for field use (pre-cached weather, saved models) - UI via **Streamlit**, backend via **FastAPI** --- ## 📁 Project Structure ``` FloodGuardEdge/ ├── app.py # Streamlit UI ├── main.py # FastAPI API backend ├── data/ │ ├── raw/ # Downloaded rasters │ └── processed/ # Cleaned CSVs, cached weather ├── models/ # Trained models (pkl, h5) ├── figures/ # Visualizations & SHAP plots ├── notebooks/ # etl.ipynb, eda.ipynb, prediction.ipynb ├── src/ │ ├── etl.py # Data collection and transformation │ ├── prediction.py # Modeling and explainability │ └── utils.py # Weather API + location features ├── tests/ # Unit tests ├── .env # API Keys ├── requirements.txt # Python dependencies └── README.md ``` ## Getting Started ```bash # Clone the repo $ git clone github.com $ cd FloodGuardEdge # Install dependencies $ pip install -r requirements.txt # Set your OpenWeatherMap API Key $ echo "OPENWEATHER_API=your_key_here" > .env # Run the ETL pipeline $ python src/etl.py # Train model (or use pre-saved models) $ python src/prediction.py # Launch the Streamlit dashboard $ streamlit run app.py # Start API server $ uvicorn main:app --reload ``` --- ---