Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability by Sajid Ullah, Xiuchen Qiao, Aqil Tariq

“…Future changes in LULC and LST were predicted for 2028 and 2038 using Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models. …”
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Land Use Modeling and Predicted Ecosystem Service Value Under Different Development Scenarios: A Case Study of the Upper–Middle Yellow River Basin, China by Mingwei Ma, Yuhuai He, Yanwei Sun, Huijuan Cui, Hongfei Zang

“…The land use pattern in 2035 was predicted using Cellular Automata and Markov models under business as usual (BAU), ecological protection (EPS), and high urbanization (HUS) scenarios. …”
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Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan by Kai Lin, Qingming Zhan, Wei Xue, Yulong Shu, Yixiao Lu

“…Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. …”
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Quantifying sustainable urbanization by predictive modeling for better agricultural management: A case study in the South Asiatic Region by Kashif Ali, Jawad Ali Shah, Saif Ullah, Syed Turab Raza

“…Land use and land cover (LULC) classification maps for 2002–2022 were analyzed using remote sensing (RS) and geographic information systems (GIS) in Sahiwal, Punjab, Pakistan. Idrisi's Cellular Automata (CA)–Markov model was used to predict future scenarios. …”
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Historical and projected forest cover changes in the Mount Kenya Ecosystem: Implications for sustainable forest management by Brian Rotich, Abdalrahman Ahmed, Benjamin Kinyili, Harison Kipkulei

“…Explanatory factors of LULC change (slope, aspect, population density, proximity to rivers, roads, and towns) were used to project LULC for 2035 using Cellular Automata and Markov Chain Analysis (CA-MCA).Six LULC types (open forest, closed forest, cropland, bareland, built-up, shrubland and grassland) were successfully classified with accuracies exceeding 82.5% and Kappa coefficients above 0.77. …”
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Land use changes, green house gas emissions, and rehabilitation model of native tree species towards sustainable management by W.C. Adinugroho, H.L. Tata, . Darwo, Y. Lisnawati, H.S. Nuroniah, R. Dewi, I. Heriansyah, . Mawazin

“…This study was depicted into 3 objectives consisted of 1) Utilize spatial analysis to examine the dynamics of peatland use change and the trajectory of peatland use, as well as to identify the drivers behind these changes; 2) describe the effects of altering peatland utilization; and 3) describe seedling performance planted on the peatland forest of Jambi province, as Tanjung Jabung Barat and Tanjung Jabung Timur.METHODS: Land-use and land-cover change analysis was carried out utilizing various map resources. Cellular Automata-Markov is employed to forecast forthcoming land cover alterations by evaluating the likelihood of land cover transitions throughout a given period. …”
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