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Journal articles on the topic 'Cellular Automata-Markov'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Meng, Xiao Qian, and Yi Jin Chen. "The Analysis and Evaluation of Land Cover Change in Xining City Based on CA-Markov Model." Applied Mechanics and Materials 253-255 (December 2012): 207–10. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.207.

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Cellular automata model is a dynamic evolution model with discrete space and time, it is powerful in spatial modeling and parallel computing, so it can take advantage of workstation to simulate the transformation process of complex spatial system. Using a model based on cellular automata to forecast the development of Xining has a great benefit for the urban development. Markov Chain model is widely used in forecasting a system which is changing smoothly, the Markov Chain would provide transition rules for the cellular automata. The main task is predicting the land cover of Xining in 2020 base

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2

Chang, Chih-Hung, та Yu-Wen Chen. "The Measure-Theoretic Entropy and Topological Entropy of Actions overℤm". Journal of Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/404626.

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This paper studies the quantitative behavior of a class of one-dimensional cellular automata, named weakly permutive cellular automata, acting on the space of all doubly infinite sequences with values in a finite ringℤm,m≥2. We calculate the measure-theoretic entropy and the topological entropy of weakly permutive cellular automata with respect to any invariant measure on the spaceℤmℤ. As an application, it is shown that the uniform Bernoulli measure is the unique maximal measure for linear cellular automata among the Markov measures.

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Kang, Junfeng, Lei Fang, Shuang Li, and Xiangrong Wang. "Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework." ISPRS International Journal of Geo-Information 8, no. 10 (2019): 454. http://dx.doi.org/10.3390/ijgi8100454.

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The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the exp

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Hawkins, Jane, and Donna Molinek. "Markov cellular automata models for chronic disease progression." International Journal of Biomathematics 08, no. 06 (2015): 1550085. http://dx.doi.org/10.1142/s1793524515500850.

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We analyze a Markov cellular automaton that models the spread of viruses that often progress to a chronic condition, such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). We show that the complex dynamical system produces a Markov process at the later stages, whose eigenvectors corresponding to the eigenvalue 1 have physical significance for the long-term prognosis of the virus. Moreover we show that drug treatment leads to chronic conditions that can be modeled by Markov shifts with more optimal eigenvectors.

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Sobottka, Marcelo. "Right-permutative cellular automata on topological Markov chains." Discrete & Continuous Dynamical Systems - A 20, no. 4 (2008): 1095–109. http://dx.doi.org/10.3934/dcds.2008.20.1095.

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6

JADAWALA, Shrushti. "Cellular Automata and Markov Chain Based Urban Growth Prediction." International Journal of Environment and Geoinformatics 8, no. 3 (2021): 337–43. http://dx.doi.org/10.30897/ijegeo.781574.

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Agapie, Alexandru, Robin Höns, and Heinz Mühlenbein. "Markov Chain Analysis for One-Dimensional Asynchronous Cellular Automata." Methodology and Computing in Applied Probability 6, no. 2 (2004): 181–201. http://dx.doi.org/10.1023/b:mcap.0000017712.55431.96.

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SANCHEZ, JUAN R. "PATTERN RECOGNITION OF ONE-DIMENSIONAL CELLULAR AUTOMATA USING MARKOV CHAINS." International Journal of Modern Physics C 15, no. 04 (2004): 563–67. http://dx.doi.org/10.1142/s0129183104006029.

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A technique is presented for the identification of rule that generates a given complex pattern of linear one-dimensional cellular automata (LCA). The technique is based on the construction of a Markov transition matrix for the Markov chains that correspond to the evolution of the automaton. Such chain is generated by the evolution of a sequence of symbols representing the value of a string composed by small portion of the sites of the automaton. Excellent results are obtained for the identification of the rules that generate different complex patterns.

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Arafah, Feny, Irenius Yopy Santrum, Dedy Kurnia Sunaryo, and Hery Purwanto. "ANALISA PERBANDINGAN METODE CELLULAR AUTOMATA ANN DAN MARKOV UNTUK PREDIKSI TUTUPAN LAHAN DI KOTA BLITAR." Jurnal Tekno Global 13, no. 02 (2024): 58–65. https://doi.org/10.36982/jtg.v13i02.4853.

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ABSTRACT The development of urban areas in Blitar City, which is triggered by population growth and mobility, has caused changes in land cover, especially the reduction in rice fields due to land conversion for housing and infrastructure. As land cover changes occur significantly, it is necessary to develop methods to predict land cover, one of the methods is Cellular Automata (CA). The objectives of this study are to determine the results of land cover classification and prediction in 2024 by utilizing Sentinel 2A image data and comparing its accuracy with field data. The CA methods used are

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Mukhopadhyay, Anirban, Parimal Mondal, Jyotiskona Barik, S. M. Chowdhury, Tuhin Ghosh, and Sugata Hazra. "Correction: Changes in mangrove species assemblages and future prediction of the Bangladesh Sundarbans using Markov chain model and cellular automata." Environmental Science: Processes & Impacts 17, no. 11 (2015): 1990–91. http://dx.doi.org/10.1039/c5em90041g.

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Correction for ‘Changes in mangrove species assemblages and future prediction of the Bangladesh Sundarbans using Markov chain model and cellular automata’ by Anirban Mukhopadhyay et al., Environ. Sci.: Processes Impacts, 2015, 17, 1111–1117.

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11

Mondal, M. S., N. Sharma, M. Kappas, and P. K. Garg. "CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1585–91. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1585-2020.

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Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov

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12

Hakim, A. M. Y., B. H. Santosa, and R. Purwana. "Defining Urban Growth Boundary in Semarang City: Integrating Spatial Planning and Predictive Modeling Techniques." IOP Conference Series: Earth and Environmental Science 1443, no. 1 (2025): 012037. https://doi.org/10.1088/1755-1315/1443/1/012037.

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Abstract Understanding the maximum percentage of urban area within an administrative region, such as Semarang City, necessitates an examination of spatial planning schemes, development regulations, and local government policies. Concurrently, cellular automata and Markov chain approaches can be used to predict how cities will grow in the future accurately. This study aims to define the urban growth boundary in Semarang City by integrating spatial planning approaches with predictive modeling techniques. The Cellular automata-Markov chain (CA-MC) method predicts future urban growth developments

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13

He, Yecheng, Weicheng Wu, Xinyuan Xie, et al. "Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example." Land 12, no. 10 (2023): 1899. http://dx.doi.org/10.3390/land12101899.

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Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without con

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SÁNCHEZ, JUAN R., and RAMÓN ALONSO-SANZ. "MULTIFRACTAL PROPERTIES OF R90 CELLULAR AUTOMATON WITH MEMORY." International Journal of Modern Physics C 15, no. 10 (2004): 1461–70. http://dx.doi.org/10.1142/s0129183104006868.

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Standard Cellular Automata (CA) are ahistoric (memoryless Markov process), i.e., the new state of a cell depends on the neighborhood configuration only at the preceding time step. This article considers the fractal and multifractal properties of an extension to the standard framework of CA implemented by the inclusion of memory capabilities. Thus, in CA with memory, while the update rules of the CA remain unaltered, historic memory of all past iterations is retained by featuring each cell by a summary of all its past states. A study is made of the effect of historic memory on the multifractal

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15

Maass, Alejandro, Servet Martínez, and Marcelo Sobottka. "Limit measures for affine cellular automata on topological Markov subgroups." Nonlinearity 19, no. 9 (2006): 2137–47. http://dx.doi.org/10.1088/0951-7715/19/9/009.

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16

Mondal, Pinki, and Jane Southworth. "Evaluation of conservation interventions using a cellular automata-Markov model." Forest Ecology and Management 260, no. 10 (2010): 1716–25. http://dx.doi.org/10.1016/j.foreco.2010.08.017.

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Surabuddin Mondal, M., N. Sharma, M. Kappas, and P. K. Garg. "CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 723–29. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-723-2019.

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<p><strong>Abstract.</strong> An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover para

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Salakory, Melianus, and Heinrich Rakuasa. "Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City." Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) 12, no. 2 (2022): 372–87. http://dx.doi.org/10.29244/jpsl.12.2.372-387.

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Ambon is a city with the highest economic and population growth in Maluku Province, which makes built-up land have high and rapid growth so that it can reduce the carrying capacity of the city's land. This can lead to an imbalance between population and available housing and may result in higher population pressure on available areas. Prediction of spatial modeling is needed as a preventive measure to prevent excessive land cover changes in the future. This study aims to analyze the carrying capacity of residential areas and spatial modeling of land cover changes in Ambon City in 2010, 2015, a

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19

Troiani, Alessio. "Probabilistic Cellular Automata Monte Carlo for the Maximum Clique Problem." Mathematics 12, no. 18 (2024): 2850. http://dx.doi.org/10.3390/math12182850.

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We consider the problem of finding the largest clique of a graph. This is an NP-hard problem and no exact algorithm to solve it exactly in polynomial time is known to exist. Several heuristic approaches have been proposed to find approximate solutions. Markov Chain Monte Carlo is one of these. In the context of Markov Chain Monte Carlo, we present a class of “parallel dynamics”, known as Probabilistic Cellular Automata, which can be used in place of the more standard choice of sequential “single spin flip” to sample from a probability distribution concentrated on the largest cliques of the gra

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20

Bacani, Vitor Matheus, and Erivelton Pereira Vick. "FUTURE SCENARIO PREDICTION USING CELLULAR AUTOMATA AND MARKOV CHAIN IN EXPANSION AREA OF FORESTRY OVER BRAZILIAN CERRADO." Mercator 19, no. 11 (2019): 1–23. http://dx.doi.org/10.4215/rm2019.e18026.

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21

JACOBI, MARTIN NILSSON, and OLOF GÖRNERUP. "A SPECTRAL METHOD FOR AGGREGATING VARIABLES IN LINEAR DYNAMICAL SYSTEMS WITH APPLICATION TO CELLULAR AUTOMATA RENORMALIZATION." Advances in Complex Systems 12, no. 02 (2009): 131–55. http://dx.doi.org/10.1142/s0219525909002155.

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We present a method for identifying coarse-grained dynamics through aggregation of variables or states in linear dynamical systems. The condition for aggregation is expressed as a permutation symmetry of a set of dual eigenvectors of the matrix that defines the dynamics. The applicability of the condition is illustrated in examples from three different generic classes of reducible Markov chains: systems consisting of independent subsystems, dynamics with symmetries, and nearly decoupled Markov chains. Furthermore we show how the method can be used to coarse-grain cellular automata.

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22

Maass, Alejandro. "On the sofic limit sets of cellular automata." Ergodic Theory and Dynamical Systems 15, no. 4 (1995): 663–84. http://dx.doi.org/10.1017/s0143385700008609.

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AbstractIt is not known in general whether any mixing sofic system is the limit set of some one-dimensional cellular automaton. We address two aspects of this question. We prove first that any mixing almost of finite type (AFT) sofic system with a receptive fixed point is the limit set of a cellular automaton, under which it is attained in finite time. The AFT condition is not necessary: we also give examples of non-AFT sofic systems having the same properties. Finally, we show that near Markov sofic systems (a subclass of AFT sofic systems) cannot be obtained as limit sets of cellular automat

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Awal, Elsa Elvira, Imas Sukaesih Sitanggang, and Lailan Syaufina. "Model Prediksi Perubahan Tutupan Lahan Pada Area Kebakaran Lahan Gambut Menggunakan Model Cellular Automata Markov." Jurnal Informatika dan Teknologi Informasi 1, no. 3 (2023): 142–53. http://dx.doi.org/10.56854/jt.v1i3.141.

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During 2016 Riau Province experienced 10,676 hectares of forest and land fires on fire, where Rokan Hilir District was the largest area of forest fires that reach 3,416 hectares. The area of peatlands in Indonesia is currently experiencing degradation because of peatland fires that, result land-use, land-use changes, and forestry. The purpose of this study was to create a prediction model of land cover change in land fire areas using the Cellular Automata Markov model. The Markov Cellular Automata model is used to predict changes in land cover because this model is very suitable to be applied

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Hanafi, Fahrudin, Dinda Putri Rahmadewi, and Fajar Setiawan. "Land Cover Changes Based on Cellular Automata for Land Surface Temperature in Semarang Regency." Geosfera Indonesia 6, no. 3 (2021): 301. http://dx.doi.org/10.19184/geosi.v6i3.23471.

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Land cover changes based on cellular automata for surface temperature in Semarang Regency has increased significantly due to the continuous rise in its population. Therefore, this study aims to identify, analyze and predict multitemporal land cover changes and surface temperature distribution in 2028. Data on the land cover map were obtained from Landsat 7 and 8 based on supervised classification, while Land Surface Temperature (LST) was calculated from its thermal bands. The collected data were analyzed for accuracy through observation, while Cellular Automata - Markov Chain was used to predi

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Olusina, J. "Urban Sprawl Analysis and Transportation Using Cellular Automata and Markov Chain." Physical Science International Journal 4, no. 8 (2014): 1191–210. http://dx.doi.org/10.9734/psij/2014/9211.

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26

Sutrisno, Dewayany, Wiwin Ambarwulan, Irmadi Nahib, et al. "Cellular Automata Markov Method, An Approach for Rice Self-Sufficiency Projection." Journal of Ecological Engineering 20, no. 6 (2019): 117–25. http://dx.doi.org/10.12911/22998993/108651.

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de Oliveira Barros, Kelly, Carlos Antonio Alvares Soares Ribeiro, Gustavo Eduardo Marcatti, et al. "Markov chains and cellular automata to predict environments subject to desertification." Journal of Environmental Management 225 (November 2018): 160–67. http://dx.doi.org/10.1016/j.jenvman.2018.07.064.

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28

Hu, Xiaoli, Xin Li, and Ling Lu. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models." Sustainability 10, no. 8 (2018): 2878. http://dx.doi.org/10.3390/su10082878.

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Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions.

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Nadoushan, MozhganAhmadi, Alireza Soffianian, and Alireza Alebrahim. "Modeling Land Use/Cover Changes by the Combination of Markov Chain and Cellular Automata Markov (CA-Markov) Models." Journal of Earth, Environment and Health Sciences 1, no. 1 (2015): 16. http://dx.doi.org/10.4103/2423-7752.159922.

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Koranteng, Addo, and Tomasz Zawila-Niedzwiecki. "Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana." Folia Forestalia Polonica 57, no. 2 (2015): 96–111. http://dx.doi.org/10.1515/ffp-2015-0010.

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Abstract Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Aut

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Koranteng, Addo, and Tomasz Zawila-Niedzwiecki. "Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana." Folia Forestalia Polonica, Series A - Forestry 57 (2) (December 1, 2015): 96–111. https://doi.org/10.1515/ffp-2015-0010.

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Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automa

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Latue, Philia Christi, and Heinrich Rakuasa. "Analysis of Land Cover Change Due to Urban Growth in Central Ternate District, Ternate City using Cellular Automata-Markov Chain." Journal of Applied Geospatial Information 7, no. 1 (2023): 722–28. http://dx.doi.org/10.30871/jagi.v7i1.4653.

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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

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Kardika, Adelia Juli, Kiamah Fathirizkia Agsa Kamarati, Faradilla Faradilla, Rusdiana Ningsih, and Nur Rahma Muzaddiyah Dinata. "MODEL SKENARIO PENGGUNAAN LAHAN BERBASIS CELLULAR AUTOMATA-MARKOV CHAIN SEBAGAI SISTEM PENDUKUNG KEPUTUSAN SPASIAL UNTUK PERENCANAAN KOTA SAMARINDA." Jurnal Hutan Tropis 12, no. 4 (2024): 447. https://doi.org/10.20527/jht.v12i4.21442.

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The city of Samarinda is one of the supporting cities for Indonesia's new capital city. This will of course have an impact on increasing infrastructure development in Samarinda. Land use scenario modeling is an effective alternative that can be used to understand land conditions by analyzing and simulating future land use scenarios. The aim of this research is to create a land use scenario model which can later be used as a spatial decision support system in policy making related to Samarinda city spatial planning and sustainable governance. The method used in this research is a combination of

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Voorhees, Burton, and Catherine Beauchemin. "Point Mutations and Transitions Between Cellular Automata Attractor Basins." Complex Systems 15, no. 1 (2004): 41–78. http://dx.doi.org/10.25088/complexsystems.15.1.41.

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Transformations between attractor basins of binary cylindrical cellular automata resulting from mutations are considered. A tau-point mutation of a state consists in toggling tau sites in that state. Results of such mutations are described by a rule-dependent probability matrix. The structure of this matrix is studied in relation to the structure of the state transition diagram and several theorems relating these are proved for the case of additive rules. It is shown that the steady state of the Markov process defined by the probability matrix is always the uniform distribution over the state

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Shabani, Morteza, Shadman Darvishi, Hamidreza Rabiei-Dastjerdi, Ali Alavi, Tanupriya Choudhury, and Karim Solaimani. "An integrated approach for simulation and prediction of land use and land cover changes and urban growth (Case study: Sanandaj city in Iran)." Journal of the Geographical Institute Jovan Cvijic, SASA 72, no. 3 (2022): 273–89. http://dx.doi.org/10.2298/ijgi2203273s.

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One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logis

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WANG, FA, LI LI, JIAN-MING HU, YAN JI, RUI MA, and RUI JIANG. "A MARKOV-PROCESS INSPIRED CA MODEL OF HIGHWAY TRAFFIC." International Journal of Modern Physics C 20, no. 01 (2009): 117–31. http://dx.doi.org/10.1142/s0129183109013509.

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To provide a more accurate description of the driving behaviors especially in car-following, namely a Markov-Gap cellular automata model is proposed in this paper. It views the variation of the gap between two consequent vehicles as a Markov process whose stationary distribution corresponds to the observed gap distribution. This new model provides a microscopic simulation explanation for the governing interaction forces (potentials) between the queuing vehicles, which cannot be directly measurable for traffic flow applications. The agreement between empirical observations and simulation result

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Tariq, Aqil, and Hong Shu. "CA-Markov Chain Analysis of Seasonal Land Surface Temperature and Land Use Land Cover Change Using Optical Multi-Temporal Satellite Data of Faisalabad, Pakistan." Remote Sensing 12, no. 20 (2020): 3402. http://dx.doi.org/10.3390/rs12203402.

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Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad. This research is aimed at predicting seasonal Land-Surface-Temperature (LST) as well as Land-Use and Land-cover (LULC) with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). Landsat 5, 7 and 8 data were used for mapping seasonal LULC and LST distributions during the months of May and November for the years 1990, 1998, 2004, 2008, 2013 and 2018. A CA-Markov-Chain was devel

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Widodo, Widiyanto Hari Subagyo, and Annisaa Hamidah Imaduddina. "Pemodelan Perubahan Penggunaan Lahan – Cellular Automata di Kecamatan Asemrowo, Kota Surabaya." Jurnal Planoearth 3, no. 1 (2018): 12. http://dx.doi.org/10.31764/jpe.v3i1.214.

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Kecamatan Asemrowo memiliki perkembangan yang stagnan karena adanyapengaruh dari genang pasang air laut yang mengakibatkan penurunan nilai lahan (RDTRK UP Tambak Osowilangun, 2007). Kondisi struktur dan mofologi kota yang stagnan berpotensi untuk berubah karena adanya pembangunan pusat pertumbuhan baru yaitu Pelabuhan Teluk Lamong (Pelindo III). Penelitian ini bertujuan untuk memprediksi penggunaan lahan ke depan dengan mengakomodasikan faktor penghambat berupagenang pasang air laut dan faktor pendorong (Pelabuhan Pelindo III) menggunakan metode Markov – cellular automata.Markov- cellular auto

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Lan, Hai, Kathleen Stewart, Zongyao Sha, Yichun Xie, and Shujuan Chang. "Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study." Remote Sensing 14, no. 3 (2022): 445. http://dx.doi.org/10.3390/rs14030445.

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With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminan

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Wang, Ruci, Hao Hou, and Yuji Murayama. "Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model." Sustainability 10, no. 8 (2018): 2633. http://dx.doi.org/10.3390/su10082633.

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Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved

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Song, Wang, Zhao Yunlin, Xu Zhenggang, Yang Guiyan, Huang Tian, and Ma Nan. "Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale." Open Geosciences 12, no. 1 (2020): 626–36. http://dx.doi.org/10.1515/geo-2020-0162.

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AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data an

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Yudichandra, Fahrizal Kreshna, Widiatmaka Widiatmaka, and Syaiful Anwar. "Perubahan dan Prediksi Penggunaan Lahan Menggunakan Markov – Cellular Automata di Kota Batu." TATALOKA 22, no. 2 (2020): 202–11. http://dx.doi.org/10.14710/tataloka.22.2.202-211.

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Along with the development of Batu City as a tourist city, it is feared that there will be an increase in land use conversion from apple orchards and other agricultural land into residential and tourism land. The rate of land use change must be controlled to maintain environment sustainability. One of the effort is studying the change that occurred in the past few years. The purpose of this study were to observe land use change pattern that occured in 2006, 2012, and 2018, and to predict the land use at 2030 in Batu City. Land use prediction was evaluated with Markov – Cellular Automata models

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Yudarwati, Rani, Santun R. P. Sitorus, and Khursatul Munibah. "ARAHAN PENGENDALIAN PERUBAHAN PENGGUNAAN LAHAN MENGGUNAKAN MARKOV - CELLULAR AUTOMATA DI KABUPATEN CIANJUR." TATALOKA 18, no. 4 (2017): 211. http://dx.doi.org/10.14710/tataloka.18.4.211-221.

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Controlling the rate of land use change is necessary due to maintaining environment sustainability. One of the efforts is studying the changes that occur in the past few years. These changes can be studied by Markov - Cellular Automata model.Cianjur is one of the regency that has a high risk of landslide hazard, so it is necessary to control land use change in order to realize environmental sustainability in accordance with the spatial plan of Cianjur regency (RTRW). The purpose of this study was to see land use changes that occurred and evaluated with the spatial plan (RTRW) and also to condu

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Fitri*, Rini, Astrid Damayanti, Nur Intan Simangunsong, and Munawir ,. "Land Use Prediction Using Markov – Cellular Automata in the Peusangan Watershed, Aceh." Aceh International Journal of Science and Technology 11, no. 3 (2023): 197–209. http://dx.doi.org/10.13170/aijst.11.3.25804.

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The population growth rate is a factor that causes changes in land use that impact land ecology due to increased population activities related to social, economic, and cultural aspects. Land use change aims to develop facilities and infrastructure to meet the population's needs. This research seeks to interpret land-use changes in 1999, 2008, and 2019 and predict land use in the Peusangan watershed in 2030. The map overlay method analyzes land use change in 1999-2008, 2008-2019, and 2019-2030. In predicting land use, this study uses the Cellular Automata approach. The results of the analysis s

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MÜHLENBEIN, HEINZ, and ROBIN HÖNS. "STOCHASTIC ANALYSIS OF CELLULAR AUTOMATA WITH APPLICATION TO THE VOTER MODEL." Advances in Complex Systems 05, no. 02n03 (2002): 301–37. http://dx.doi.org/10.1142/s0219525902000596.

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We perform a rigorous stochastic analysis of both deterministic and stochastic cellular automata. The theory uses a mesoscopic view, i.e. it works with probabilities instead of individual configurations used in micro-simulations. An exact stochastic analysis can be done using the theory of Markov processes. But this analysis is restricted to small problems only. For larger problems we compute the distribution using a factorization into marginals. These marginals are then approximated by the given marginals of low order with iterative proportional fitting using the maximum entropy principle. Th

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Koko, Auwalu Faisal, Wu Yue, Ghali Abdullahi Abubakar, Roknisadeh Hamed, and Akram Ahmed Noman Alabsi. "Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)." Sustainability 12, no. 24 (2020): 10452. http://dx.doi.org/10.3390/su122410452.

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Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classifica

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Mehdi, Esnaashari, and Reza Meybodi Mohammad. "Irregular Cellular Learning Automata." IEEE Transactions on Cybernetics 45, no. 8 (2014): 1622–32. https://doi.org/10.1109/TCYB.2014.2356591.

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Cellular learning automaton (CLA) is a recently introduced model that combines cellular automaton (CA) and learning automaton (LA). The basic idea of CLA is to use LA to adjust the state transition probability of stochastic CA. This model has been used to solve problems in areas such as channel assignment in cellular networks, call admission control, image processing, and very large scale integration placement. In this paper, an extension of CLA called irregular CLA (ICLA) is introduced. This extension is obtained by removing the structure regularity assumption in CLA. Irregularity in the stru

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Saputra, Aswin Nur, Muhammad Wahyu Saputra, Hendy Fatchurohman, and Prama Ardha Aryaguna. "Modeling of Land Cover Changes in Banjarbaru City South Kalimantan Province." JURNAL GEOGRAFI 16, no. 1 (2024): 89. http://dx.doi.org/10.24114/jg.v16i1.48121.

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Urban areas often experience land cover changes. Banjarbaru is one of several cities in Indonesia that has experienced land changes. The relocation of the administrative center of Banjarmasin City to Banjarbaru City led to the development of settlements. One spatial analysis carried out to examine the phenomenon of land change is remote sensing techniques. The method that can be used is the Land Change Modeler from MOLUSCE in QGIS. This model uses the CAM (Cellular Automata Markov) method to identify land cover change and predict land cover distribution. CAM can understand and predict land cha

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Casse, Jérôme. "Probabilistic cellular automata with general alphabets possessing a Markov chain as an invariant distribution." Advances in Applied Probability 48, no. 2 (2016): 369–91. http://dx.doi.org/10.1017/apr.2016.5.

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Abstract This paper is devoted to probabilistic cellular automata (PCAs) on N,Z or Z / nZ, depending on two neighbors with a general alphabet E (finite or infinite, discrete or not). We study the following question: under which conditions does a PCA possess a Markov chain as an invariant distribution? Previous results in the literature give some conditions on the transition matrix (for positive rate PCAs) when the alphabet E is finite. Here we obtain conditions on the transition kernel of a PCA with a general alphabet E. In particular, we show that the existence of an invariant Markov chain is

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Septory, Juan Steiven Imanuel, Philia Chisti Latue, and Heinrich Rakuasa. "Model Dinamika Spasial Perubahan Tutupan Lahan dan Daya Dukung Lahan Permukiman Kota Ambon Tahun 2031." GEOGRAPHIA : Jurnal Pendidikan dan Penelitian Geografi 4, no. 1 (2023): 51–62. http://dx.doi.org/10.53682/gjppg.v4i1.5801.

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The rate of population growth that continues to increase in Ambon city with the availability of suitable land is relatively constant, resulting in inconsistencies between land requirements and available land. This study aims to analyze land cover changes in Ambon City in 2013, 2018, 2023 and predict land cover in 2031 using the Cellular Automata Markov Chain (CAMC) and the carrying capacity index of residential areas. The driving factors used in this study were elevation, slope, distance from the coastline, distance from the main road, distance from the river, distance from the center of econo

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