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

2019 ◽  
Vol 23 (4) ◽  
pp. 672-687 ◽  
Author(s):  
Min Cao ◽  
Mengxue Huang ◽  
Ruqi Xu ◽  
Guonian Lü ◽  
Min Chen
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Integrated Model ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Growth Simulation ◽  
Simulation And Optimization

scholarly journals Implementation and calibration of a new irregular cellular automata-based model for local urban growth simulation: The MUGICA model

2017 ◽  
Vol 46 (2) ◽  
pp. 243-263 ◽  
Author(s):  
Pablo Barreira-González ◽  
Francisco Aguilera-Benavente ◽  
Montserrat Gómez-Delgado
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Confusion Matrix ◽  
Good Choice ◽  
Random Component ◽  
Simulation Accuracy ◽  
Growth Simulation ◽  
Geographic Space ◽  
Irregular Structure ◽  
Geographical Environment

Cellular automata-based models have traditionally employed regular grids to represent the geographical environment when simulating urban growth or land use change. Over the last two decades, the scientific community has introduced the use of other spatial structures in an attempt to represent the processes simulated by these models more realistically. Cadastre parcels are a good choice when simulating urban growth at local scales, where pixels or regular cells do not represent the geographic space properly. Furthermore, the implementation and calibration of key factors such as accessibility and suitability have not been sufficiently explored in models employing irregular structures. This paper presents a fully calibrated model to simulate urban growth: Model for Urban Growth simulation using Irregular Cellular Automata. The model uses the irregular structure of the cadastre and its smallest unit: the cadastral parcel. The factors included are based on the traditional Neighbourhood, Accessibility, Suitability and Zoning Status modelling schema, frequently employed in other models. Each factor was implemented and calibrated for the irregular structure employed by the model, and a new approach was explored to introduce a random component that would reproduce illegal growth. Several versions of Model for Urban Growth simulation using Irregular Cellular Automata were produced to calibrate the model within the period 2000–2010. The results obtained from the simulations were compared against observed growth for 2010, adapting the traditional confusion matrix to irregular space. A new metric is proposed, called growth simulation accuracy, which measures how well the model locates urban growth.

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