Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Milad Asadi (), Amir Oshnooei-Nooshabadi, Samira-Sadat Saleh (), Fattaneh Habibnezhad, Sonia Sarafraz-Asbagh and John Lodewijk Van Genderen
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Milad Asadi: Department of Urban Planning, Daneshpajoohan Pishro Higher Education Institute, Isfahan 81747-47144, Iran
Amir Oshnooei-Nooshabadi: Department of Geography, Lorestan University, Khorramabad 68151-44316, Iran
Samira-Sadat Saleh: College of Science, North Texas University, Denton, TX 76203, USA
Fattaneh Habibnezhad: Department of Geography and Urban Planning, Kharazmi University, Tehran 31979-37551, Iran
Sonia Sarafraz-Asbagh: Department of Geography and Urban Planning, University of Tabriz, Tabriz 51666-16471, Iran
John Lodewijk Van Genderen: International Institute for Geo-Information Science and Earth Observation (ITC), 7500 Enschede, The Netherlands

Sustainability, 2022, vol. 14, issue 23, 1-16

Abstract: Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Keywords: LULC; urban sprawling; artificial neural network; CA–Markov; Urmia (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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