An interactive, full-stack time-series analytics application built to visualize, track, and forecast regional wildfire metrics and risk levels.
FireWatch provides data-driven situational awareness by analyzing time-series environmental data and modeling wildfire propagation trends. Through an interactive web dashboard, users can explore historical burn areas, assess current weather indicators, and view predictive forecasts for wildfire risk assessment.
- Frontend / Dashboard Framework: Streamlit
- Core Language: Python
- Data Processing & Analytics: Pandas, NumPy
- Machine Learning & Forecasting: Scikit-Learn, Statsmodels
- Data Visualization: Plotly, Matplotlib, Seaborn
- Interactive Wildfire Dashboard: Dynamic map and time-series visualizers powered by Streamlit for easy exploration of geospatial and environmental metrics.
- Time-Series Forecasting: Statistical and predictive modeling to estimate regional fire trends based on historical weather, temperature, and drought indicators.
- Risk Factor Analysis: Comprehensive breakdown of key risk drivers, including temperature fluctuations, humidity levels, and wind speeds.
- Data Pipeline: Standardized data intake, cleaning, and preprocessing pipeline for multi-source environmental datasets.
├── data/ # Wildfire and weather datasets (gitignored if large)
├── notebooks/ # Exploratory data analysis and time-series model testing
├── app.py # Main Streamlit application entry point
├── src/ # Data cleaning, feature engineering, and model logic
├── requirements.txt # Python package dependencies
└── README.md # Project documentation