Calibrating a cellular automata model for understanding rural-urban land conversion: A Pareto front-based multi-objective optimization approach

Kai Cao, Bo Huang, Manchun Li, WenWen Li

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Cellular automata (CA) modeling is useful to assist in understanding rural-urban 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.

Original languageEnglish (US)
Pages (from-to)1028-1046
Number of pages19
JournalInternational Journal of Geographical Information Science
Volume28
Issue number5
DOIs
StatePublished - May 2014

Keywords

  • Logit regression
  • NSGA-II
  • calibration
  • cellular automata
  • land conversion
  • rural-urban

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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