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|Scopus||Web of Science®||Altmetric|
|Title:||Multi-objective optimisation framework for calibration of Cellular Automata land-use models|
van Delden, H.
|Citation:||Environmental Modelling and Software, 2018; 100:175-200|
|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.|
|Appears in Collections:||Civil and Environmental Engineering publications|
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