Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118590
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Empirically derived method and software for semi-automatic calibration of Cellular Automata land-use models
Author: Newland, C.
Zecchin, A.
Maier, H.
Newman, J.
van Delden, H.
Citation: Environmental Modelling and Software, 2018; 108:208-239
Publisher: Elsevier
Issue Date: 2018
ISSN: 1364-8152
1873-6726
Statement of
Responsibility: 
Charles P. Newland, Aaron C. Zecchin, Holger R. Maier, Jeffrey P. Newman, Hedwig van Delden
Abstract: Land-use change models generally include neighbourhood rules to capture the spatial dynamics between different land-uses that drive land-use changes, introducing many parameters that require calibration. We present a process-specific semi-automatic method for calibrating neighbourhood rules that utilises discursive knowledge and empirical analysis to reduce the complexity of the calibration problem, and efficiently calibrates the remaining interactions with consideration of locational agreement and landscape pattern structure objectives. The approach and software for implementing it are tested on four case studies of major European cities with different physical characteristics and rates of urban growth, exploring preferences for different objectives. The approach outperformed benchmark models for both calibration and validation when a balanced objective preference was used. This research demonstrates the utility of process-specific calibration methods, and highlights how process knowledge can be integrated with automatic calibration to make it more efficient.
Keywords: Cellular automata; land-use model; calibration complexity reduction; semi-automatic calibration; automatic parameter tuning
Rights: © 2018 Elsevier Ltd. All rights reserved.
RMID: 0030098083
DOI: 10.1016/j.envsoft.2018.07.013
Appears in Collections:Civil and Environmental Engineering publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.