Genetic programming for cellular automata urban inundation modelling
Gibson, M; Keedwell, EC; Savic, D
Date: 17 August 2014
Conference paper
Publisher
CUNY Academic Works
Related links
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). ...
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 statetransition
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 approache
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0