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. Issue 4 (3rd April 2018)

• Record Type: Journal Article
• Authors: Şalap-Ayça, Seda; Jankowski, Piotr; Clarke, Keith C; Kyriakidis, Phaedon C; Nara, Atsushi
• Is Part Of: International journal of geographical information science. Volume 32:Issue 4(2018)
• Page Start: 637
• Page End: 662
• Publication Date: 2018-04-03
• Subjects: Land-use change -- urban growth -- sensitivity analysis -- meta-modeling -- polynomial chaos expansion; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1406944
• Languages: English
• ISSNs: 1365-8816

Abstract

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.