Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth

Ye Zhou, Feng Zhang, Zhenhong Du, Xinyue Ye and Renyi Liu
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Ye Zhou: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Feng Zhang: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Zhenhong Du: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Renyi Liu: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China

Sustainability, 2017, vol. 9, issue 10, 1-19

Abstract: Sustainable urban development is a focus of regional policy makers; therefore, how to measure and understand urban growth is an important research topic. This paper quantified the amount of urban growth on land use maps that were derived from multi-temporal Landsat images of Jiaxing City as a rapidly-growing city in Zhejiang Province from 2000–2015. Furthermore, a new approach coupled the heuristic bat algorithm (BA) and deep belief network (DBN) with the cellular automata (CA) model (DBN-CA), which was developed to simulate the urban expansion in 2015 and forecast the distribution of urban areas of Jiaxing City in 2024. The BA was proposed to obtain the best structure of the DBN, while the optimized DBN model considered the nonlinear spatial-temporal relationship of driving forces in urban expansion. Comparisons between the DBN-CA and the conventional artificial neural network-based CA (ANN-CA) model were also performed. This study demonstrates that the proposed model is more stable and accurate than the ANN-CA model, since the minimum and maximum values of the kappa coefficient of the DBN-CA were 77.109% and 78.366%, while the ANN-CA’s values were 63.460% and 76.151% over the 200 experiments, respectively. Therefore, the DBN-CA model is a potentially effective new approach to survey land use change and urban expansion and allows sustainability research to study the health of urban growth trends.

Keywords: urban growth simulation; cellular automata; bat algorithm; deep belief network; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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