Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

ABSTRACT

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.

KEYWORDS:

• Urban expansion
• artificial neural network
• cellular automata
• Markov chain
• kappa simulation
• South Auckland

Acknowledgments

Thanks to Dr E. Goldstein for assisting with manuscript editing.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Notes on contributors

Tingting Xu

Tingting Xu is a Ph.D. student in the school of environment, the University of Auckland. His research is focused on advanced geographical information science technology with machine learning algorithms, remote sensing, and dynamic urban modeling (cellular automata and agent-based model).

Jay Gao

Jay Gao is an Associate Professor in the school of environment, the University of Auckland. His research is focused on quantitative remote sensing and integration of geo-computational methods.

Giovanni Coco

Giovanni Coco is an Associate Professor in the school of environment, the University of Auckland. His research is focused on complexity and pattern formation and machine learning.