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https://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. |
DOI: | 10.1016/j.envsoft.2017.11.012 |
Published version: | http://dx.doi.org/10.1016/j.envsoft.2017.11.012 |
Appears in Collections: | Aurora harvest 8 Civil and Environmental Engineering publications |
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