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 <i>self-modifying</i> constants, <i>Monte Carlo</i> runs and <i>critical slope</i> to determine their influence on model calibration performance. Calibration performance has been examined in terms of statistical measures i.e., urban <i>area</i>, <i>clusters</i>, <i>edges</i>, <i>mean cluster size</i>, and <i>cluster radius</i>, 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 <i>self-modifying parameters</i> as {1.3, 0.10, 0.90, and 1.25} for <i>boom</i>, <i>bust</i>, <i>critical low</i> and <i>critical high</i> respectively; <i>Monte Carlo</i> runs as sixty (60) and <i>critical slope</i> as 15 to simulate the urban growth of the study area.