Multi-objective optimisation framework for calibration of Cellular Automata land-use models
Date
2018
Authors
Newland, C.
Maier, H.
Zecchin, A.
Newman, J.
van Delden, H.
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Journal article
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Environmental Modelling and Software, 2018; 100:175-200
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Charles P. Newland, Holger R. Maier, Aaron C. Zecchin, Jeffrey P. Newman, Hedwig van Delden
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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.
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© 2017 Elsevier Ltd. All rights reserved.