- author = "Mike J. Gibson and Edward C. Keedwell and Dragan A. Savic",
- title = "Genetic Programming For Cellular Automata Urban Inundation Modelling",
- booktitle = "11th International Conference on Hydroinformatics",
- year = "2014",
- address = "New York, USA",
- month = aug # " 17-21",
- organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-0-692-28129-1",
-
URL = "
http://www.hic2014.org/proceedings/bitstream/handle/123456789/1650/1723.pdf",
- size = "8 pages",
-
abstract = "Recent advances in Cellular Automata (CA) represent a
new, computationally efficient method of simulating
flooding in urban areas. A number of recent
publications in this field have shown that CAs can be
much more computationally efficient than methods that
use standard shallow water equations (Saint
Venant/Navier-Stokes equations). CAs operate using
local state-transition rules that determine the
progression of the flow from one cell in the grid to
another cell, and in a number of publications the
Manning's Formula is used as a simplified local state
transition rule. Through the distributed interactions
of the CA, computationally simplified urban flooding
can be simulated, although these methods are limited by
the approximation represented by the Manning's
formula.
An alternative approach is to learn the state transition rule using an artificial intelligence approach. One such approach is Genetic Programming (GP) that has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Manning's formula and findings indicate that the GP rules can improve on these approaches.",
- notes = "Broken June 2021 http://www.hic2014.org/xmlui/",
