Optimization of adaptive neuro fuzzy inference system based urban growth model

Abstract

Background: Global urban population has increased from 22.9 % in 1985 to 47 % in 2010. In Iran, population living in urban areas has consistently increased from about 31 % in 1956 to 68.4 % in 2006. Urban growth as one of the results of rapid population growth, results lots of problems. Thus, monitoring and modelling of the urban expansion is necessary. Methods: In this research, a novel Adaptive Neuro Fuzzy Inference System (ANFIS)-based methodology has been developed for urban growth modeling, as well as interpreting the relationship between the drivers of urbanization. Then, ANFIS results were compared with those achieved by both ANN and Logistic Regression (LR)-based methodologies using Percent Area Match quantity and Percent Area Match location to assess model goodness of fit. Results: The proposed ANFIS model which takes the advantages of using neural networks and fuzzy logic at the same time, had the best performances among the three implemented models. It was able to identify important factors in the development and their relationship and influence on the growth of the city. Conclusions: The research aim is to find a computational based method which can effectively capture, analyse and model the complex nature of spatial phenomenon like urban growth. The proposed ANFIS method due to its structure is able to deals with nonlinear phenomenon. Integration of Remote sensing data, GIS tools and also, computational based method provide us an effective, reliable and also, scientific methods for monitoring, analysing and modeling of environmental phenomenon.