This project simulates urban expansion using a hybrid approach that combines Cellular Automata (CA) and Machine Learning (ML). Synthetic data for slope, distance to roads, and population density were used to train a Random Forest classifier, and urban growth was modeled over time using CA.
- Synthetic data generation for terrain and urban features
- Random Forest model for urban growth prediction
- Cellular Automata simulation for spatial expansion
- Visualization of initial vs. future urban maps
- Export of synthetic dataset in Excel format
- numpy
- pandas
- matplotlib
- scikit-learn
Ifunanya Osondu