Dynamic simulation of urban growth and land use change using an integrated cellular automata and markov chain models: a case of Bahir Dar city, Ethiopia

Getu, Kenu; Bhat, H. Gangadhara

doi:10.1007/s12517-022-10304-1

Cited by 7 publications

(4 citation statements)

References 111 publications

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“…The MC model, which is a theory based on the process of the formation of Markov random process systems for the prediction and optimal control theory method, is utilized to calculate the quantity transfer rules, resulting in the generation of a transfer probability matrix and a transfer area matrix [44]. It is commonly used to predict geographical characteristics, especially in scenarios without an aftereffect event, making it a crucial method in geographic research.…”

Section: Quantity Transfer Rules and Markov Chain Modelmentioning

confidence: 99%

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

Wen,

Wang,

Song

et al. 2024

Atmosphere

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The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region.

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Section: Quantity Transfer Rules and Markov Chain Modelmentioning

confidence: 99%

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

Wen,

Wang,

Song

et al. 2024

Atmosphere

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“…This model illustrates the dynamics of LULC, vegetation, the expansion of urbanized areas, and watershed planning modeling. It is essential for planning and developing various land-use policies that will promote appropriate LULC management [66]. (see Figure 2).…”

Section: Lulc Prediction Using the Ca-markov Chain Modelmentioning

confidence: 99%

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Gasirabo,

Xi,

Hamad

et al. 2023

Land

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Over the past few decades, the growth of population and the development of the economy have had a significant impact on the way land is used and covered (LULC) in the Nile Nyabarongo River basin. However, there is limited knowledge about the patterns of land use and the mechanisms that drive changes in these patterns because of human activities. Therefore, it is crucial to examine how land use and cover are shifting in this area, identify the factors responsible for these changes, and forecast future patterns. This study sought (1) to evaluate the changes in LULC from 1990 to 2020 and (2) to predict future fluctuations until 2060. By analyzing the LULC data for the years 1990, 2000, 2010, and 2020, this study simulated the future LULC patterns of the area for the next 30 years using the LULC transition matrix and the Markov chain model. The study classified the LULC into five categories: forestland, grassland, cropland, settlement, and water. The results revealed that there will be significant changes in the LULC of the study area from 2030 to 2060. The forest area is projected to decrease by 801.7 km, 771.6 km, and 508.2 km, while the cropland area will expand by 6307.2 km, 6249.2 km, and 6420.6 km during this period. The grassland area will experience a small increase of 761.1 km, 802.4 km, and 859.1 km, and the settlement area will also grow by 355.2 km, 407.4 km, and 453.2 km. In contrast, the water area will decrease by 55.9 km, 50.5 km, and 40 km. The ongoing pattern of LULCC is expected to persist over the next three decades, with an increase in cropland area and grassland. This study’s findings can provide valuable insights for land use planners and water resource managers in developing fair land use and water resource management policies for the entire region, enabling them to make well-informed decisions.

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“…Utilization of Geographic Information System (GIS) and remote sensing data can be used to develop a sustainable urban planning system in the future (Mohamed & Worku, 2020) Dynamic spatial modeling plays an important role in urban planning, namely predicting urban growth patterns (Han and Jia, 2017;Akbar and Supriatna, 2019;, therefore dynamic spatial modeling is highly recommended for use in simulation and prediction of urban trends. Dynamic spatial modeling using the CA-Markov method or Celular Automata-Markov Chain has become one of the main dynamic models used by most researchers in recent years in the fields of geography, environmental science and urban and regional planning Getu and Bhat, 2022) According to Kushwaha et al, (2021) The CA-Markov model is a commonly used model in the last five years due to its simplicity and can be easily integrated with other models. Xu et al, (2022) add that the CA-Markov model greatly provides ease of use and simplicity of implementation, as well as its expansion and the ability to add influencing variables in the simulation process.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Cover Change Due to Urban Growth in Central Ternate District, Ternate City using Cellular Automata-Markov Chain

Latue

Rakuasa

2023

JAGI

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The increase in population and the increasing flow of urbanization in Central Ternate District make the need for land to live also increase as a result of which there will be inconsistencies or inequalities between land needs and available land, a decrease in environmental carrying capacity and environmental damage in the future. This study aims to analyze changes in land cover due to urban growth in Central Ternate District, Ternate City using The Automata- Markov Chain. Cellular Automata- Markov Chain is used to analyze and predict land cover changes in 2002, 2012, 2022 and 2031. The results showed that residential land will continue to experience an increase in area along with population growth and the high demand for land to settle. The results of this study are expected to be input in policy making related to the arrangement and utilization of space in The Central Ternate District in the future.

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Dynamic simulation of urban growth and land use change using an integrated cellular automata and markov chain models: a case of Bahir Dar city, Ethiopia

Cited by 7 publications

References 111 publications

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Analysis of Land Cover Change Due to Urban Growth in Central Ternate District, Ternate City using Cellular Automata-Markov Chain

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