@article {bnh-5072,
	title = {Multiple objective optimisation framework for automatic parameter tuning of Cellular Automata land-use models with multiple dynamic land-use classes},
	journal = {Environmental Modelling and Software},
	volume = {100},
	year = {2018},
	month = {02/2018},
	pages = {175-200},
	chapter = {175},
	abstract = {<p><span>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 tradeoff 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.</span></p>},
	doi = {https://doi.org/10.1016/j.envsoft.2017.11.012},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815217306862?via\%3Dihub},
	author = {Charles Newland and Hedwig van Delden and Jeffrey Newman and Aaron Zecchin and Holger Maier}
}
