Title: Identification and prediction of land use changes based on artificial neural network and CA-Markov for sustainable land-use planning

Authors: Amanehalsadat Pouriyeh

Addresses: Department of Environmental Science, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract: This study aims to model land-use changes of a city in the centre of Iran during 1991-2021 using the artificial neural network and the CA-Markov models. The land-use maps were prepared by the maximum likelihood method. The land use map of 2001-2013 was used to model the land-use changes of 2021 and predict the land use map of 2030. The results of the prediction based on the two models showed that urban development will occur with the conversion of mountainous areas and barren lands and gardens and agricultural lands. The city's growth towards barren lands and mountainous areas makes the region prone to floods.

Keywords: artificial neural network; CA-Markov; land change modeller; LCM; maximum likelihood algorithm; land use change; change detection; land change prediction; remote sensing; flood; Yazd; Iran.

DOI: 10.1504/IJGW.2023.132272

International Journal of Global Warming, 2023 Vol.30 No.4, pp.349 - 366

Received: 22 Jun 2022
Received in revised form: 06 Dec 2022
Accepted: 11 Dec 2022
Published online: 14 Jul 2023 *

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