Genetic programming for cellular automata urban inundation modelling

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 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