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.
Highlights
A scientific understanding of land use change and urban expansion is required for policy makers to strategize and facilitate sustainable urbanization development
This study demonstrates that the proposed model is more stable and accurate than the artificial neural networks (ANNs)-cellular automata (CA) model, since the minimum and maximum values of the kappa coefficient of the deep belief network (DBN)-CA were 77.109% and 78.366%, while the artificial neural network-based CA (ANN-CA)’s values were 63.460% and 76.151% over the 200 experiments, respectively
Our research aims to propose a DBN-based CA (DBN-CA) model to simulate the spatial-temporal changes in non-urban land use, while the metaheuristic bat algorithm (BA) developed
Summary
A scientific understanding of land use change and urban expansion is required for policy makers to strategize and facilitate sustainable urbanization development. Only a few studies have combined DBN with the CA model to simulate land use change or urban growth. Riccioli utilized CA and Markov chains to examine the land use changes from 1990–2000, and it was validated for 2006 [48] In this context, our research aims to propose a DBN-based CA (DBN-CA) model to simulate the spatial-temporal changes in non-urban land use, while the metaheuristic bat algorithm (BA) developed. The DBN-CA model involving data processing, DBN calibration, model validation and prediction is tested using a case study of urban growth for Jiaxing City, Zhejiang Province, China, in 2000, 2008, 2015 and 2024 We compare it to the ANN-based CA (ANN-CA) and demonstrate the effective simulation ability of the DBN-CA for the study area.
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