Integrating cellular automata, artificial neural network and fuzzy set theory to simulate threatened orchards: Application to Maragheh, Iran

Abstract

Urbanization processes challenge the growth of orchards areas in many cities in Iran. In Maragheh orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study aims, first, to develop a new model by coupling cellular automata and artificial neural network with fuzzy set theory (CA-ANN-Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal non-linearities of the driving forces underlying the urban growth processes. Second, CA-ANN-Fuzzy model is compared with two existing approaches, namely a basic cellular automata (CA) and a cellular automata coupled with an artificial neural network (CA-ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the up-coming years up to 2025. Results show that CA-ANN-Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA-ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades while the CA-ANN-Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.