Spatiotemporal dynamics of urban expansion and its simulation using CA-ANN model in Ulaanbaatar, Mongolia

Gantumur, Byambakhuu; Wu, Falin; Vandansambuu, Battsengel; Tsegmid, Bazarkhand; Dalaibaatar, Enkhjargal; Zhao, Yan

doi:10.1080/10106049.2020.1723714

Cited by 28 publications

(7 citation statements)

References 55 publications

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“…The combined methodological framework of AHP and MOLUSCE Plugin for the purpose of spatial and temporal prediction of GPZs represents a novel insight in groundwater potential mapping. The duo AHP-MOLUSCE plugin was used by (Gantumur et al, 2022) in the estimation of urban development suitability area. The adaptability of the MOLUSCE plugin was demonstrated by its combined use with the DRASTIC method in prediction of GPZs for 2042 by (Boitt et…”

Section: Forecasting 2030 and 2050 Gpzsmentioning

confidence: 99%

Mapping Groundwater Potential Zones with GIS-RS-AHP Under Climate Change: Case of Mostaganem Plateau, Northwest Algeria

Chemirik,

Baahmed,

Nedjai

et al. 2024

Ecol. Eng. Environ. Technol.

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In arid regions with increasing water needs due to growing populations and agriculture, heightened by climate change, groundwater arises as a crucial asset. This research evaluated climate change influence on groundwater potential zones (GPZs) during 2000 and 2014, within the Mostaganem plateau's alluvial aquifer in Algeria, using a methodology that integrates analytical hierarchy process (AHP), remote sensing (RS) and geographic information system (GIS). Forecasts for 2030 and 2050 were conducted using the QGIS MOLUSCE plugin. Findings reveal a (30.29%) decrease in zones of moderate potential, the vanishing of high potential sectors, alongside a (7.53%) and a (22.1%) rise in fair potential and low potential, respectively, from 2000 to 2014. Between 2014 and 2030, fair and moderate potential decrease by 6.62% and 0.48%, while low potential zones see a 7.47% increase. These shifts are linked to changes in rainfall distribution, and land use land cover (LULC), notably intensive agriculture of herbaceous crops. Slight changes are anticipated between 2030 and 2050, possibly due to the onset of a resilience equilibrium from 2030 onwards. These findings are crucial as a preliminary investigation, highlighting the necessity of optimal groundwater management.

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Section: Forecasting 2030 and 2050 Gpzsmentioning

confidence: 99%

Mapping Groundwater Potential Zones with GIS-RS-AHP Under Climate Change: Case of Mostaganem Plateau, Northwest Algeria

Chemirik,

Baahmed,

Nedjai

et al. 2024

Ecol. Eng. Environ. Technol.

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“…Nowaday's various updated models are used in LULC prediction. For example; ANN model [ 22 ]; Markov model [ 23 ]; CA-Markov model [ 24 ]; CA-ANN model [ 25 , 26 ]; MLP-ANN model [ 27 ]; Multi-model ensemble [ [28] , [29] , [30] ], CA-ANN MOLUSCE model [ 31 ] and to simulate the spatiotemporal changes inside the research region, an integrated CA-artificial neural network (ANN) model using MOLUSCE was adopted for the current investigation. Additionally, we examined spatial driving factors for land surface dynamics in the district using the Geodetector tool.…”

Section: Introductionmentioning

confidence: 99%

Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

Islam¹,

Karim²,

Rashid³

2023

Heliyon

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“…Gantumur et al. (2020) calculated the ULD by using the analytic hierarchy process method and realized spatial simulation and UGB delimitation through an artificial neural network‐cellular automata (ANN‐CA) model. Tariq and Shu (2020) estimated the future ULD by using Markov chains and simulated the spatial pattern of urban land based on the CA model.…”

Section: Introductionmentioning

confidence: 99%

“…Liang et al (2018) estimated the ULD based on the system dynamics model and then performed morphological dilation and erosion on the simulation results of the Future Land Use Simulation (FLUS) model to delineate the UGBs. Gantumur et al (2020) calculated the ULD by using the analytic hierarchy process method and realized spatial simulation and UGB delimitation through an artificial neural network-cellular automata (ANN-CA) model. Tariq and Shu (2020) estimated the future ULD by using Markov chains and simulated the spatial pattern of urban land based on the CA model.…”

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confidence: 99%

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

et al. 2022

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To limit urban sprawl and reduce the negative impacts of urban expansion, the United States has taken the lead in delimiting urban growth boundaries (UGBs), that is, setting boundaries around cities and limiting urban expansion within such boundaries (Daniels, 2001). This approach can effectively protect the cropland and natural habitat around cities (Wei & Ye, 2014), alleviating the contradiction between urban development and the conservation of resources and environment to realize sustainable development (Jiang, Chen, et al., 2016;Long et al., 2013;Tayyebi et al., 2011). In China, where the urban land area (ULA) is increasing rapidly, delimiting UGBs has

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Spatiotemporal dynamics of urban expansion and its simulation using CA-ANN model in Ulaanbaatar, Mongolia

Cited by 28 publications

References 55 publications

Mapping Groundwater Potential Zones with GIS-RS-AHP Under Climate Change: Case of Mostaganem Plateau, Northwest Algeria

Mapping Groundwater Potential Zones with GIS-RS-AHP Under Climate Change: Case of Mostaganem Plateau, Northwest Algeria

Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

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