Abstract
This paper presents a method to optimise the calibration of parameters and land use transition rules of a cellular automata (CA) urban growth model using a self-adaptive genetic algorithm (SAGA). Optimal calibration is achieved through an algorithm that minimises the difference between the simulated and observed urban growth. The model was applied to simulate land use change from non-urban to urban in South East Queensland’s Logan City, Australia, from 1991 to 2001. The performance of the calibrated model was evaluated by comparing the empirical land use change maps from the Landsat imagery to the simulated land use change produced by the calibrated model. The simulation accuracies of the model show that the calibrated model generated 86.3% correctness, mostly due to observed persistence being simulated as persistence and some due to observed change being simulated as change. The 13.7% simulation error was due to nearly equal amounts of observed persistence being simulated as change (7.5%) and observed change being simulated as persistence (6.2%). Both the SAGA-CA model and a logistic-based CA model without SAGA optimisation have simulated more change than the amount of observed change over the simulation period; however, the overestimation is slightly more severe for the logistic-CA model. The SAGA-CA model also outperforms the logistic-CA model with fewer quantity and allocation errors and slightly more hits. For Logan City, the most important factors driving urban growth are the spatial proximity to existing urban centres, roads and railway stations. However, the probability of a place being urbanised is lower when people are attracted to work in other regions.
Highlights
Many cellular automata (CA)-based urban models have been developed and applied in various situations to simulate the dynamic change of urban land use over time
It remains challenging for urban modellers to identify suitable transition rules reflecting the driving factors on land use change in the modelling practice
This paper contributes to the field by developing an urban CA model with its transition rules optimised by a self-adaptive genetic algorithm (SAGA)
Summary
Cellular automata (CA) models have been increasingly applied to simulate the systematic spatio-temporal processes and the stochastic behaviour of land use change and urban growth [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18].Central to a CA-based urban model is the definition of the model’s transition rules, which determine how the state of a cell changes over time [9,10,11,15]. According to [19], the transition rules of urban CA models can be classified into five categories, including (1) strictly orthodox transition rules [9,20]; (2) rules based on key drivers [5,21]; (3) rules based on artificial intelligence [22]; (4) fuzzy logic transition rules [6,11,12,16,23]; and (5) other types of transition rules [24,25,26] Of these five categories, rules based on key drivers have been the most widely applied, which require strict definitions of a number of variables and parameters representing various spatial and non-spatial factors driving urban growth. A diverse range of statistical methods have been developed to select such variables and parameters; these include logistic regression [5], spatial logistic regression [27], multi-criteria evaluation (MCE) [28]
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