@article {bnh-4336, title = {Multi-objective optimisation framework for calibration of Cellular Automata land-use models}, journal = {Environmental Modelling \& Software}, volume = {100}, year = {2018}, month = {02/2018}, pages = {175-200}, chapter = {175}, abstract = {

Modelling of land-use change plays an important role in many areas of environmental planning. However, land-use change models remain challenging to calibrate, as they contain many sensitive parameters, making the calibration process time-consuming. We present a multi-objective optimisation framework for automatic calibration of Cellular Automata land-use models with multiple dynamic land-use classes. The framework considers objectives related to locational agreement and landscape pattern structure, as well as the inherent stochasticity of land-use models. The framework was tested on the Randstad region in the Netherlands, identifying 77 model parameter sets that generated a Pareto front of optimal trade-off solutions between the objectives. A selection of these parameter sets was assessed further based on heuristic knowledge, evaluating the simulated output maps and parameter values to determine a final calibrated model. This research demonstrates that heuristic knowledge complements the evaluation of land-use models calibrated using formal optimisation methods.

}, doi = {10.1016/j.envsoft.2017.11.012}, url = {https://www.sciencedirect.com/science/article/pii/S1364815217306862}, author = {Charles Newland and Holger Maier and Aaron Zecchin and Jeffrey Newman and Hedwig van Delden} }