Sensitivity analysis and retrieval of optimum SLEUTH model parameters

The Cellular Automata (CA) based SLEUTH model has emerged as a widely applied model to many cities for land use land cover (LULC) change and urban growth modelling due to its simplicity, robustness, and ease of implementation. The present study employed a rigorous sensitivity testing of self-modifying constants, Monte Carlo runs and critical slope to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban area, clusters, edges, mean cluster size, and cluster radius, best model fitness measure (i.e., Optimal SLEUTH Metrics (OSM)), overall accuracy percentage and hit-miss-false alarm method have been used. The sensitivity analysis reveals the optimum values for self-modifying parameters as {1.3, 0.10, 0.90, and 1.25} for boom, bust, critical low and critical high respectively; Monte Carlo runs as sixty (60) and critical slope as 15 to simulate the urban growth of the study area.