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Sustainability 2017, 9(10), 1786; doi:10.3390/su9101786

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

1
School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
2
Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China
3
Department of Geography, Kent State University, Kent, OH 44240, USA
*
Author to whom correspondence should be addressed.
Received: 20 August 2017 / Revised: 29 September 2017 / Accepted: 29 September 2017 / Published: 2 October 2017
(This article belongs to the Section Sustainable Urban and Rural Development)
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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. View Full-Text
Keywords: urban growth simulation; cellular automata; bat algorithm; deep belief network; artificial neural network urban growth simulation; cellular automata; bat algorithm; deep belief network; artificial neural network
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Zhou, Y.; Zhang, F.; Du, Z.; Ye, X.; Liu, R. Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth. Sustainability 2017, 9, 1786.

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