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|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
Clarke, Keith C
|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|
|Appears in Collections:||Άρθρα/Articles|
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