@article {Cao:2014:1365-8816:1028,
title = "Calibrating a cellular automata model for understanding ruralurban land conversion: a Pareto front-based multi-objective optimization approach",
journal = "International Journal of Geographical Information Science",
parent_itemid = "infobike://tandf/tgis",
publishercode ="tandf",
year = "2014",
volume = "28",
number = "5",
publication date ="2014-05-04T00:00:00",
pages = "1028-1046",
itemtype = "ARTICLE",
issn = "1365-8816",
eissn = "1365-8824",
url = "https://www.ingentaconnect.com/content/tandf/tgis/2014/00000028/00000005/art00012",
doi = "doi:10.1080/13658816.2013.851793",
keyword = "cellular automata, Logit regression, rural–urban, calibration, land conversion, NSGA-II",
author = "Cao and Huang and Li and Li",
abstract = "Cellular automata (CA) modeling is useful to assist in understanding ruralurban land conversion processes. Although CA calibration is essential to ensuring an accurate modeling outcome, it remains a significant challenge. This study aims to address that challenge by developing
and evaluating a multi-objective optimization model that considers the objectives of minimizing minus maximum likelihood estimation (MLE) value and minimizing number of errors (NOE) when calibrating CA transition rules. A Pareto front-based heuristic search algorithm, the Non-dominated Sorting
Genetic Algorithm-II (NSGA-II), is used to obtain optimal or near-optimal solutions. The proposed calibration approach is validated using a case study from New Castle County, Delaware, United States. A comparison of the NSGA-II-based calibration model, the generic Logit regression calibration
approach (MLE-based Generic Genetic Algorithm (GGA) calibration approach), and the NOE-based GGA calibration approach demonstrates that the proposed calibration model can produce stable solutions with better simulation accuracy. Furthermore, it can generate a set of solutions with different
preferences regarding the two objectives which can provide CA simulation with robust parameters options.",
}