Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/119064
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Type: Journal article
Title: Multi-objective optimisation framework for calibration of Cellular Automata land-use models
Author: Newland, C.
Maier, H.
Zecchin, A.
Newman, J.
van Delden, H.
Citation: Environmental Modelling and Software, 2018; 100:175-200
Publisher: Elsevier
Issue Date: 2018
ISSN: 1364-8152
1873-6726
Statement of
Responsibility: 
Charles P. Newland, Holger R. Maier, Aaron C. Zecchin, Jeffrey P. Newman, Hedwig van Delden
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.
Keywords: Cellular Automata; land-use model; automatic calibration; automatic parameter adjustment; multi-objective optimisation
Rights: © 2017 Elsevier Ltd. All rights reserved.
RMID: 0030078498
DOI: 10.1016/j.envsoft.2017.11.012
Appears in Collections:Civil and Environmental Engineering publications

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