Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/10932
Title: A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model
Authors: Şalap-Ayça, Seda 
Jankowski, Piotr 
Clarke, Keith C 
Kyriakidis, Phaedon 
Nara, Atsushi 
Keywords: Land-use change;Meta-modeling;Polynomial chaos expansion;Sensitivity analysis;Urban growth
Category: Civil Engineering;Civil Engineering
Field: Engineering and Technology
Issue Date: 3-Apr-2018
Publisher: Taylor and Francis Ltd.
Source: International Journal of Geographical Information Science, 2018, Volume 32, Issue 4, Pages 637-662
DOI: https://doi.org/10.1080/13658816.2017.1406944
Abstract: The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models.
URI: http://ktisis.cut.ac.cy/handle/10488/10932
ISSN: 13658816
Rights: © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Type: Article
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