Li X, Yeh A G-O, 2001, "Calibration of cellular automata by using neural networks for the simulation of complex urban systems" Environment and Planning A 33(8) 1445 – 1462
Download citation data in RIS format
Calibration of cellular automata by using neural networks for the simulation of complex urban systems
Xia Li, Anthony Gar-On Yeh
Received 22 November 2000; in revised form 14 May 2001
Abstract. This paper presents a new cellular automata (CA) model which uses artificial neural networks tlsb>for both calibration and simulation. A critical issue for urban CA simulation is how to determine parameter values and define model structures. The simulation of real cities involves the use of many variables and parameters. The calibration of CA models is very difficult when there is a large set of parameters. In the proposed model, most of the parameter values for CA simulation are automatically determined by the training of artificial neural networks. The parameter values from the training are then imported into the CA model which is also based on the algorithm of neural networks. With the use of neural networks, users do not need to provide detailed transition rules which are difficult to define. The study shows that the model has better accuracy than traditional CA models in the simulation of nonlinear complex urban systems.
Full-text PDF size: 425 Kb
References 29 references, 7 with DOI links ()
Your computer (IP address: 18.104.22.168) has not been recognised as being on a network authorised to view the full text or references of this article. This content is part of our deep back archive. If you are a member of a university library that has a subscription to the journal, please contact your serials librarian (subscriptions information).