Description:
This paper provides a comprehensive review on the development of urban growth models including associated theories and applications. Starting from 1920s, through the application of top-down approaches, linear system theories for theoretical or physical models to the 1980s through application of bottom-up approaches, then transition to complex system-based models and the evolution and improvement of these models within the last 20 years has been discussed. Three popular models for urban growth modelling namely Cellular Automata (CA), Agent Based Model (ABM), and Artificial Neural Networks (ANN) have been described briefly. The explanation on why and how these models were improvised to better simulate Urban growth has been discussed. The inefficiencies of these models as individual models and how integrated models can resolve this have been briefly discussed and via this discussion, the connection of the models in the chronology of evolution have been shown. This paper concludes that evolution and improvisation of models focuses mainly to increase the model component inefficiencies to reflect the true nature of growth and incorporate various factors influencing growth. Also, application of different Urban growth models depends on the objectives of modeling and data availability