Modeling Urban Growth and Land Use Change with Cellular Automata and Genetic Algorithms

It is a well accepted fact that urbanisation, climate change and population growth represent an enormous challenge for urban water managers. In this respect, computer models coupled with spatial mapping techniques have proved to be invaluable. The present paper demonstrates the use of a cellular automata model (Dinamica Ego) for modeling land use change process on a case study of Birmingham (UK). Two approaches were evaluated for this purpose. The results obtained show that the model based on optimization of parameters that deal with the process of expansion/contraction is capable of producing promising results. The analysis was carried out using the Corine dataset for the years 1990 and 2000 and the fuzzy similarity test was used as the objective function. To minimize computational demands the optimization loop was simulated with the NSGA II algorithm using the parallel computing approach.