A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

Cao, Min; Bennett, Sean J.; Shen, Quanfei; Xu, Ruqi

doi:10.1080/13658816.2016.1151521

Cited by 20 publications

(12 citation statements)

References 47 publications

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“…Studies have shown that the impact factors for urban growth can be described by a large number of spatial variables such as distance, neighbors, and some physical properties (Cao et al, ). In this study, the GWO algorithm is employed to discover optimal urban growth rules from the spatial variables by simulating the social behavior of grey wolves, including hierarchy and group hunting.…”

Section: Discovering City Growth Rules By Gwomentioning

confidence: 99%

“…These algorithms derive from the burst collective intelligence of simple agent groups (Bonabeau, Dorigo, & Theraulaz, ) and are often inspired by natural swarm behaviors of social animals, such as ants, birds, fish, and fireflies. A variety of SI algorithms have been employed to derive the transition rules, such as particle swarm optimization (Feng, Liu, Tong, Liu, & Deng, ; Liao et al, ), ant colony optimization (Liu, Li, Liu, He, & Ai, ; Liu, Li, Yeh, He, & Tao, ), simulated annealing (Feng & Liu, ), artificial bee colony (Yang, Tang, Cao, & Zhu, ), cuckoo search (Cao et al, ), and bat algorithms (Cao, Bennett, Shen, & Xu, ).…”

Section: Introductionmentioning

confidence: 99%

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A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

Cao

Huang

et al. 2019

Transactions in GIS

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This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolfinspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWOoptimized urban growth rules for CA describe the relationship between the spatial variables and the urban land-use status for each cell in the formation of "if-then." The GWO algorithm and CA model are then integrated as the GWO-CA model for urban growth simulation and optimization.By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO-CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management. | 673 CAO et al.

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Section: Discovering City Growth Rules By Gwomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

Cao

Huang

et al. 2019

Transactions in GIS

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“…It is also difficult to tackle a series of complex behaviors associated with natural systems. To overcome this problem, a series of machine-learning algorithms have been proposed, assuming that the historic geographical processes remain stable for a certain period in the future through local interaction between cells, such as logistic regression [8][9][10], artificial neural networks [11][12][13][14], support vector machines [15][16][17], decision trees [18,19], random forests [20,21], genetic algorithms [22,23], and swarm-intelligence algorithms [24][25][26][27][28]. Although the algorithms above show significant improvement in defining nonlinear transition rules for CA, there still remain many problems like overfitting, easily resulting in local optima and weak in global searching.…”

Section: Introductionmentioning

confidence: 99%

Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China

Ou¹,

Yang²,

Du³

et al. 2019

Sustainability

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An effective simulation of the urban sprawl in an urban agglomeration is conducive to making regional policies. Previous studies verified the effectiveness of the cellular-automata (CA) model in simulating urban sprawl, and emphasized that the definition of transition rules is the key to the construction of the CA model. However, existing simulation models based on CA are limited in defining complex transition rules. The aim of this study was to investigate the capability of two unsupervised deep-learning algorithms (deep-belief networks, DBN) and stacked denoising autoencoders (SDA) to define transition rules in order to obtain more accurate simulated results. Choosing the Beijing–Tianjin–Tangshan urban agglomeration as the study area, two proposed models (DBN–CA and SDA–CA) were implemented in this area for simulating its urban sprawl during 2000–2010. Additionally, two traditional machine-learning-based CA models were built for comparative experiments. The implementation results demonstrated that integrating CA with unsupervised deep-learning algorithms is more suitable and accurate than traditional machine-learning algorithms on both the cell level and pattern level. Meanwhile, compared with the DBN–CA, the SDA–CA model had better accuracy in both aspects. Therefore, the unsupervised deep-learning-based CA model, especially SDA–CA, is a novel approach for simulating urban sprawl and also potentially for other complex geographical phenomena.

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“…Over the past two decades, remarkable achievements have been made in geographical CA-based dynamic urban growth and land use change modeling, particularly in rapidly urbanizing areas [11][12][13][14][15][16]. Substantial progress has also been made in CA methodology, including transition rules retrieval, neighborhood configuration, scale effects, and results assessment [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Feng

Liu

Chen

et al. 2016

IJGI

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Abstract:We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios.

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A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

Cited by 20 publications

References 47 publications

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

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