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 in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) 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 nonlinearities of the driving forces underlying the urban growth processes. Second, the CA-ANN-Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA-ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming 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.

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

“…In the classical set theory, the relationship between the elements and the set has only two possibilities of "yes" or "no" (that element x ⊂ U or element x⊄U exists in two cases). However, in the fuzzy set theory, the relationship between the element and the set has some or more subordinate relationships [65][66][67][68][69][70]. In order to accurately describe the difference between the classical set theory and the fuzzy set theory, we define the symbol space: the domain U, the fuzzy set A, and the element x x ⊂ U ; the relationship between the element and the fuzzy set can be described as…”

Long-Term Land Cover Data for the Lower Peninsula of Michigan, 2010–2050

“…The multilayer perceptron ANN consists of one input layer, one hidden layer and one output layer. In this study, the number of nodes set for the input and hidden layers was the same as the number of driving forces [36], while we used one node for the output layer. The output node of the ANN was coded with either 1 (for cells converted from other land cover classes to urban classes) or 0 (for cells remaining in same land cover class) between 1992 and 2001.…”

“…The result of this step is called a suitability map, where cell values vary from 0 (least likely to convert to urban) to 1 (most likely to convert to urban) and describe the probability of urbanization [26]. While the suitability map determines the potential for urbanization, the LTM also requires a quantity of urbanization to convert the suitability map to a simulated binary map with values of 0, indicating non-urban, or 1, indicating urbanization [36]. These quantities calculated as a result of comparison between the land cover maps in 1992 and 2001 ( Figure 2E).…”

“…The LTM has been used to forecast land cover change in a variety of areas across the world, such as the United States [1,29], Europe [30] eastern Africa [23] and Asia [31]. Primarily, the LTM has been used to (1) determine the uncertainty levels of land change model outputs at a variety of spatial-temporal scales and land change contexts [32]; (2) couple other process-based models to understand how land cover alters climate [33,34] and water [26,35] and ecosystem dynamics; and (3) generate baseline data layers for online decision making tools [36,37]. Here, we applied LTM to produce urbanization maps for the Lower Peninsula of Michigan in United States from 2010 to 2050 with five-year intervals.…”

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

“…The repertoire of ML is large (Lim et al 2000, Fernández-Delgado et al 2014, which makes the selection of a well-performing model a challenging task. However, frequently only a single approach is applied (Azari et al, 2016, Huang et al 2010, Hagenauer and Helbich 2012, Linard et al 2013, Samardžić-Petrović et al 2016, Omrani et al 2019, often either artificial neural networks (Haykin 2009), random forests (Breiman 2001), and support vector machines (Scholkopf and Smola 2001). To support an evidence-based algorithmic selection, a small number of land-use studies have compared multiple ML algorithms (Rogan et al 2008, Tayyebi and Pijanowski 2014, Samardžić-Petrović et al 2017, Shafizadeh-Moghadam et al 2017.…”