Urban growth modelling in Qazvin, Iran: an investigation into the performance of three ANFIS methods

ABSTRACT

Determining the rules that lead to the expansion of urban areas has always been a challenging factor in urban modeling. To overcome this issue, an ANFIS model is proposed to enhance the simulation of urban growth through automatic production of transition rules. Hence, 22 ANFIS models were trained using different division methods and a Cellular Automata-based Markov Chain (CA-MC) was developed to examine their efficiencies. The results of accuracies reveal that the ANFIS accompanied by subtractive clustering (ANFIS-SC) simulating urban growth with a Kappa coefficient of 0.76 and overall accuracy of 93.41% is superior to other ANFIS and CA-MC models.

KEYWORDS:

• Adaptive network-based fuzzy inference system
• Fuzzy c-means
• Markov Chain
• subtractive clustering
• urban expansion
• urban sprawl

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary materials

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14498596.2022.2066579.