Journal articles: 'CA Markov prediction'

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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 (August 6, 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 and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

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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 (October 13, 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 experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

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Ildoromi, Alireza, and Mahtab Safari Shad. "Land Use Change Prediction using a Hybrid (CA-Markov) Model." Ecopersia 5, no. 1 (March 1, 2017): 1631–40. http://dx.doi.org/10.18869/modares.ecopersia.5.1.1631.

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Li, Xiuquan, Meizhen Wang, Xuejun Liu, Zhuan Chen, Xiaojian Wei, and Weitao Che. "MCR-Modified CA–Markov Model for the Simulation of Urban Expansion." Sustainability 10, no. 9 (August 31, 2018): 3116. http://dx.doi.org/10.3390/su10093116.

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Ecosystem balance is an important factor that affects healthy and sustainable urban development. The traditional cellular automata (CA) model considers only a few ecological factors, however, the MCR model can account for ecological factors. In previous studies, few ecological factors were added to the CA model. Thus, the minimal cumulative resistance (MCR) model is combined with the CA and Markov models for the simulation of urban expansion. To verify the reliability of the method, the Wuhan metropolitan area was selected as a representative urban area, and its expansion in the past and future was simulated. Firstly, seven influential factors were selected from the perspective of location theory. The transformation rules of the comprehensive resistance surface followed by the modified CA–Markov model were constructed on the basis of the MCR model. The expansion of the Wuhan metropolitan area in 2013 was simulated on the basis of the 1996 and 2006 maps of land-use status, and the kappa coefficient was used as an index to evaluate the accuracy of the proposed method. Then, the expansion of the Wuhan metropolitan area in 2020 was simulated. Finally, the simulation results obtained with and without the MCR model were compared and analysed from the macro- and micro levels. Results show that the prediction accuracy of the two models differed for ecological regions, such as woodlands and water bodies. The similarities between the regions that were overestimated and underestimated by the MCR-modified CA–Markov model and non-MCR model may be attributed to solution of the land-use transfer matrix with the Markov model. The accuracy of the MCR-modified CA–Markov model for predicting forests, water and other ecological regions was higher than that of the Markov model. Therefore, the proposed MCR-modified CA–Markov model has potential applications in environmentally-conscious urban expansion.

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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 model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

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ایلدرمی, علیرضا, حمید نوری, مهین نادری, سهیلا آقابیگی امین, and حسین زینی وند. "Land use change prediction using Markov chain and CA Markov Model (Case Study: Gareen Watershed)." journal of watershed management research 8, no. 16 (February 1, 2018): 232–40. http://dx.doi.org/10.29252/jwmr.8.16.232.

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Park, Geun-Ae, and Seong-Joon Kim. "Prediction of the Urbanization Progress Using Factor Analysis and CA-Markov Technique." Journal of The Korean Society of Agricultural Engineers 49, no. 6 (November 30, 2007): 105–14. http://dx.doi.org/10.5389/ksae.2007.49.6.105.

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Ge, Yuan, Yan Zhang, Wengen Gao, Fanyong Cheng, Nuo Yu, and Jincenzi Wu. "Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs." Discrete Dynamics in Nature and Society 2020 (October 29, 2020): 1–16. http://dx.doi.org/10.1155/2020/6848420.

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This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. Based on this assumption, the double-chain hidden Markov model (DCHMM) is proposed in this paper to model the delays. There are two Markov chains in this model. One is the hidden Markov chain which consists of the network states and the other is the observable Markov chain which consists of the delays. Moreover, the delays are also affected by the hidden network states, which constructs the DCHMM-based delay model. The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Some comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed method.

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Liu, Pei, Shoujun Jia, Ruimei Han, and Hanwei Zhang. "Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach." Journal of Sensors 2018 (June 4, 2018): 1–14. http://dx.doi.org/10.1155/2018/1058513.

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In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CA-Markov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.

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Babaeian Diva, A., B. Bigdeli, and P. Pahlavani. "AGRICULTURAL LAND CHANGE DETECTING AND FORECASTING USING COMBINATION OF FEEDFORWARD MULTILAYER NEURAL NETWORK, CELLULAR AUTOMATA AND MARKOV CHAIN MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 153–58. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-153-2019.

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Abstract. This paper proposed a methodology for finding changes in agricultural land of Tehran during past years and simulating these changes for future years. The proposed method utilized the spatial GIS-based techniques and Landsat satellite imagery to predict agricultural land map for the future of Tehran. Therefore, a method for finding and predicting changes based on combining the feedforward multilayer perceptron neural network (MLP), cellular automata (CA), and Markov chain model were applied. In this regard, the Landsat images of 2002, 2008, and 2014 were classified by a binary support vector machine classifier into two classes of agricultural and non-agricultural. Then, the potential transition maps were generated by the neural network MLP and extensible areas were obtained by the Markov chain model. Finally, the results of these two steps were combined with the MOLA method and the 2020 and 2025 agricultural maps were predicted. The proposed method obtained the Kappa factor of 89.92% that indicates the high ability of the neural network and the CA–Markov for finding the changes and prediction in the city of Tehran.

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Park, Min-Ji, Geun-Ae Park, Yong-Jun Lee, and Seong-Joon Kim. "Application of the Modified CA-Markov Technique for Future Prediction of Forest Land Cover in a Mountainous Watershed." Journal of The Korean Society of Agricultural Engineers 52, no. 1 (January 31, 2010): 61–68. http://dx.doi.org/10.5389/ksae.2010.52.1.061.

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Sathiracheewin, Supalak, Vichai Surapatana, and Dulpichet Rerkpreedapong. "Land-Use Change Prediction by CA-Markov Method for Electric Load Density Map." International Review on Modelling and Simulations (IREMOS) 8, no. 4 (August 31, 2015): 436. http://dx.doi.org/10.15866/iremos.v8i4.6557.

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Mondal, M. S., N. Sharma, M. Kappas, and P. K. Garg. "CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS AND EFFECT OF NUMERICAL ITERATIONS, IMAGE INTERVAL (TIME STEPS) ON PREDICTION RESULTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 713–20. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-713-2020.

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Abstract. In this study, land use land cover (LULC) map of 1987, 1997 and 2007 derived from digital satellite images of Landsat - 5 TM of 1987, IRS-1C LISS III images of 1997 and IRS-P6 LISS III images of 2007, respectively, to monitor and predict future LULC scenario. Future LULC has been predicted using CA Markov Model for 2017, 2027 and 2050 by using LULC map of 1987 & 1997, 1997 & 2007 and 1987 & 2007. The period (image interval / time steps) between different predictions years (i.e., 2017, 2027 and 2050 using 1987 & 1997, 1997 & 2007 and 1987 & 2007 LULC) are 10, 20, 30, 43 and 53 years. The number of iteration was based on the time steps i.e., iterations 20 to predict LULC for 2017 (prediction from 1997 to 2017); iterations 30 for 2027 (prediction from 1997 to 2027); iterations 53 for 2050 (prediction from 1997 to 2050). The relationships (correlations) of quantity of predicted LULC found strong positive correlation for three time periods (0.981, 0.984, 0.966 for 2017, 0.981, 0.984, 0.975 for 2027, 0.977, 0.987, and 0.980 for 2050) and established that there are almost no effect in quantity of prediction results of different time steps (iterations) images and time intervals are used to predict future LULC. The predicting location of predicted LULC of 2017, 2027 & 2050 for the three cases showing positive correlation, where r are 0.728, 0.758 and 0.708 for 2017 – when relatively less time steps used; r are 0.696, 0.761 and 0.674 for 2027 – when medium time steps used; r are 0.599, 0.721, 0.574 for 2050 – when more time steps used. The analysis established that although there are nearly no effect on quantitative prediction results but have small impact of iterations (time steps) and time intervals on spatial distribution of predicted LULC results.

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Otuoze, Suleiman Hassan, Dexter V. L. Hunt, and Ian Jefferson. "Predictive Modeling of Transport Infrastructure Space for Urban Growth Phenomena in Developing Countries’ Cities: A Case Study of Kano — Nigeria." Sustainability 13, no. 1 (December 31, 2020): 308. http://dx.doi.org/10.3390/su13010308.

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Global urbanization has the most tremendous negative effects on the changing landscapes in many developing countries’ cities. It is necessary to develop appropriate monitoring techniques for tracking transport space evolution. The work explores the impacts of urban growth dynamics of transport space over the past decades as a basis for predicting future space demands in Kano, Nigeria. Three epochs of Landsat images from 1984, 2013 and 2019 were processed, classified and analyzed. Spatial classifications of land-use/land-cover (LULC) types in Kano include transport space, built-up areas, vegetation, farmland, bare land and water. The data analysis involves model calibration, validation and prediction using areas using the hybrid modeling techniques—cellular automata-Markov (CA-Markov) in IDIRISI SELVA 17.0 and remote-sensing ARC-GIS 10.7 softwares. The result finds significant expansion of transport and built-up areas while other LULC receded throughout the entire study period. Predictive modeling of transport infrastructure shows spatial expansion by 345 km2 (3.9%) and 410 km2 (11.7%) in 2030 and 2050 respectively. Kappa reliability indices of agreement (KIA) classified images and ground maps were 85%, 86% and 88%, respectively, for 1984, 2013 and 2019 time series. The calibration quality met the 80% minimum suggested in literature for the spatial-temporal track and prediction of urban growth phenomena.

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Aguejdad, Rahim. "The Influence of the Calibration Interval on Simulating Non-Stationary Urban Growth Dynamic Using CA-Markov Model." Remote Sensing 13, no. 3 (January 29, 2021): 468. http://dx.doi.org/10.3390/rs13030468.

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The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi’s Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy. The quantity disagreement was analyzed by taking into consideration the temporal non-stationarity of change rate over the calibration and the prediction intervals, the model ability to reproduce the past amount of change in the future, and the time duration of the prediction interval. The results show that the calibration interval significantly influenced the amount and the spatial allocation of the estimated change. In addition to that, the spatial allocation of change using CA-Markov depended highly on the basis land cover image rather than the observed transition during the calibration period. Therefore, this study provides useful insights on the role of the training dates in the simulation of non-stationary LUCC.

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Su Lei, Zhu Jinghai, Ren Shaohong, Hu Yuanman, and Liu Miao. "Landscape Pattern Change Prediction Of Jinhu Coastal Area Based On Logistic-CA-Markov Model." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 11 (June 30, 2012): 1–10. http://dx.doi.org/10.4156/aiss.vol4.issue11.1.

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Wang, Shixu, Zulu Zhang, and Xue Wang. "Land use change and prediction in the Baimahe Basin using GIS and CA-Markov model." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012074. http://dx.doi.org/10.1088/1755-1315/17/1/012074.

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Mondal, Md Surabuddin, Nayan Sharma, P. K. Garg, and Martin Kappas. "Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results." Egyptian Journal of Remote Sensing and Space Science 19, no. 2 (December 2016): 259–72. http://dx.doi.org/10.1016/j.ejrs.2016.08.001.

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Abdulrahman, Ashti I., and Shamal A. Ameen. "Predicting Land use and land cover spatiotemporal changes utilizing CA-Markov model in Duhok district between 1999 and 2033." Academic Journal of Nawroz University 9, no. 4 (September 29, 2020): 71. http://dx.doi.org/10.25007/ajnu.v9n4a892.

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The process of spatiotemporal changes in land use land cover (LULC) and predicting their future changes are highly important for LULC managers. one of the most important challenges in LULC studies is considered to be the creation of simulation of future change in LULC that involve spatial modeling. the purpose of this study is to use GIS and remote sensing to classify LULC classes in Duhok district between 1999 and 2018, and their results calculated using an integrated cellular automaton and ca-markov chain model to simulate LULC changes in 2033. in this study, satellite images from landsat7 ETM and landsat8 oli used for Duhok district which is located in the northern part of Iraq obtained from united states geological survey (USGS) for the periods (1999 and 2018) analyzed using remote sensing and GIS techniques in addition to the ground control points, for each class 60 ground points have taken. to simulate future LULC changes for 2033, integrated approaches of cellular automata and ca-markov models utilized in Idrisi selva software. the outcomes demonstrate that Duhok district has experienced a total of 184.91km changes during the period (table 4). the prediction also indicates that the changes will equal to 235.4 km by 2033 (table 8). soil and grass constitute the majority of changes among LULC classes and are increasing continuously. the achieved kappa values for the model accuracy assessment higher than 0.93 and 0.85 for 1999 and 2018 respectively showed the model’s capability to forecast future LULC changes in Duhok district. thus, analyzing trends of LULC changes from past to now and predict future applying ca-markov model can play an important role in land use planning, policy making, and managing randomly utilized LULC classes in the proposed study area.

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Onilude, Olalekan O., and Eric Vaz. "Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model." Sci 3, no. 2 (May 5, 2021): 23. http://dx.doi.org/10.3390/sci3020023.

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Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.

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Bharath, S., K. S. Rajan, and T. V. Ramachandra. "Status and future transition of rapid urbanizing landscape in central Western Ghats - CA based approach." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 69–75. http://dx.doi.org/10.5194/isprsannals-ii-8-69-2014.

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The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA–Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA–Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004–2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36% (1973 to 2013) and increase in agriculture land as well as built-up area of 8.65% (2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management approaches based on the modelled future impacts. This necessitates immediate measures for minimizing the future impacts.

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Dang, Xuewei, Liang Zhou, Xiaoen Li, Haowei Mu, Lei Che, and Fuwei Qiao. "Simulation and prediction of Shanghai urban spatial change based on random forest and CA-Markov model." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-53-2019.

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Abstract. In the context of rapid urbanization, accurate assessment of urban expansion has become increasingly important for urban sustainable development, and smart growth theory has been put forward to avoid urban sprawl. Previous studies about urban expansion simulation focused only on ecological constrain which prevent urban growth from developing in specific regions. However, government decision-making and urban planning greatly influence urban development and limit the disorderly expansion of the urban. In this paper, we consider planning policies into urban simulation and uses the ecological protection red line, farmland protection red line and cultural protection control line as limiting factors for future urban simulation. Choosing Shanghai as the study area, we integrated Random Forests Algorithm (RFA), Markov chain and Cellular Automata (CA) to simulate urban expansion in 2015, and further predict the urban expansion in 2020, 2025 and 2030. The results show that the overall accuracy of urban land use simulation in 2015 is 93.86%, and the kappa coefficient is 0.8577. The model has a good simulation effect. Furthermore, the predicted results in 2020, 2025 and 2030 show that the urban land area in Shanghai is still increasing, and the spatial distribution of urban land has obvious circle structure and regional differences. The urban areas within 10km from the city center are growing slowly, and the region within 30km from the city center is growing faster, and there are more new urban points from 2025 to 2030. But in the area 30km away from the city center, different administrative areas show different urban growth phenomena. Among them, there are a large number of new urban points in the junction area between Songjiang District and Jinshan District, which may be the focus of future urban development planning in Shanghai. The proposed model and the results can help planners study the evolution of urban patterns and develop further urban planning.

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Achmad, A., M. Irwansyah, and I. Ramli. "Prediction of future urban growth using CA-Markov for urban sustainability planning of Banda Aceh, Indonesia." IOP Conference Series: Earth and Environmental Science 126 (March 2018): 012166. http://dx.doi.org/10.1088/1755-1315/126/1/012166.

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Risma, H. Zubair, and Paharuddin. "Prediction of land use and land cover (LULC) changes using CA-Markov model in Mamuju Subdistrict." Journal of Physics: Conference Series 1341 (October 2019): 082033. http://dx.doi.org/10.1088/1742-6596/1341/8/082033.

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Wang, Shufang, Xiyun Jiao, Liping Wang, Aimin Gong, Honghui Sang, Mohamed Khaled Salahou, and Liudong Zhang. "Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis." Sustainability 12, no. 4 (February 13, 2020): 1396. http://dx.doi.org/10.3390/su12041396.

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The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

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Ramadhan, Kurniawan, and Supriatna. "Erosion rate prediction model ssing SWAT and CA-Markov methods (case study: Upper Ci Catih Catchment Area)." IOP Conference Series: Earth and Environmental Science 311 (August 14, 2019): 012072. http://dx.doi.org/10.1088/1755-1315/311/1/012072.

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Yu, Yuhan, Mengmeng Yu, Lu Lin, Jiaxin Chen, Dongjie Li, Wenting Zhang, and Kai Cao. "National Green GDP Assessment and Prediction for China Based on a CA-Markov Land Use Simulation Model." Sustainability 11, no. 3 (January 22, 2019): 576. http://dx.doi.org/10.3390/su11030576.

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Green Gross Domestic Product (GDP) is an important indicator to reflect the trade-off between the ecosystem and economic system. Substantial research has mapped historical green GDP spatially. But few studies have concerned future variations of green GDP. In this study, we have calculated and mapped the spatial distribution of the green GDP by summing the ecosystem service value (ESV) and GDP for China from 1990 to 2015. The pattern of land use change simulated by a CA-Markov model was used in the process of ESV prediction (with an average accuracy of 86%). On the other hand, based on the increasing trend of GDP during the period of 1990 to 2015, a regression model was built up to present time-series increases in GDP at prefecture-level cities, having an average value of R square (R2) of approximately 0.85 and significance level less than 0.05. The results indicated that (1) from 1990 to 2015, green GDP was increased, with a huge growth rate of 78%. Specifically, the ESV value was decreased slightly, while the GDP value was increased substantially. (2) Forecasted green GDP would increase by 194978.29 billion yuan in 2050. Specifically, the future ESV will decline, while the rapidly increased GDP leads to the final increase in future green GDP. (3) According to our results, the spatial differences in green GDP for regions became more significant from 1990 to 2050.

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Zhang, Ze, Baoqing Hu, Weiguo Jiang, and Haihong Qiu. "Identification and scenario prediction of degree of wetland damage in Guangxi based on the CA-Markov model." Ecological Indicators 127 (August 2021): 107764. http://dx.doi.org/10.1016/j.ecolind.2021.107764.

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Hong, Xiaochun, and Xiang Ji. "Prediction of Underground Space Development Function of Existing Industrial District in City Based on CA-Markov Model." E3S Web of Conferences 237 (2021): 04019. http://dx.doi.org/10.1051/e3sconf/202123704019.

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Scientific analysis of the spatial evolution of existing industrial areas in cities and prediction of future development needs will help to rationally allocate land resources in existing industrial areas in urban renewal, scientifically and rationally develop underground space, and promote the sustainable development of existing industrial areas. First of all, the development mode and leading function type of the existing industrial zone in the city are sorted out, and its corresponding underground space development function is further sorted out. It is found that the underground space development of the existing industrial zones in the city is closely related to the dominant functions and location of the ground renewal. To scientifically guide the development of underground space in existing industrial areas in cities, this study proposes a method based on the dynamics model and the CA-Markov model to predict the functions of underground space development in existing industrial areas in cities, which will improve the efficiency and Benefits to promote the rational allocation of land resources is of great significance.

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Kundu, Krishan, Prasun Halder, and Jyotsna Kumar Mandal. "Detection and Prediction of Sundarban Reserve Forest using the CA-Markov Chain Model and Remote Sensing Data." Earth Science Informatics 14, no. 3 (July 17, 2021): 1503–20. http://dx.doi.org/10.1007/s12145-021-00648-9.

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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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Abstract. 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 parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.

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Subedi, Praveen, Kabiraj Subedi, and Bina Thapa. "Application of a Hybrid Cellular Automaton – Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida." Applied Ecology and Environmental Sciences 1, no. 6 (November 28, 2013): 126–32. http://dx.doi.org/10.12691/aees-1-6-5.

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Sun, Xian Bin. "Changes of Land Use and its Prediction Based on CA-Markov Model in the Coastal Zone of Yancheng." Advanced Materials Research 726-731 (August 2013): 4929–32. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4929.

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For studying the influence of human activity on the change of land use type and ecological environment in coastal region, the coastal zone of Yancheng was selected to analyze the change of land use and spatial landscape pattern in the past 17 years by using geographic information system (GIS). The Markov model was applied to predict land use variation in the following 10 years. The results showed that: The land use structure in the coastal areas of Yancheng has undergone great changes from 1991 to 2008. It is demonstrated that the percentage of farmland area ascended from 36.42%. to 50.17%., and the artificial wetland area ascended from 9.96% to 20.22%,while the percentage of natural wetland area declined from 46.41% to 23.01%. Construction land increased five times (85.29km2) . Nature wetland, farmland and artificial wetland were the main land use types of research area with obvious reciprocal transformation. Nature wetland was mainly transferred to farmland and artificial wetland, and farmland was mainly transferred to artificial wetland and construction land in 1991-2008. The three types of time-space distribution extended to the coastal zone gradually. And the land exploitation activities became increasingly intensive. A series of landscape patterns changed because of severe human disturbance, such as obvious fragmentation, the dominance decrease, and the diversity and evenness increase, the degree of space across of the nature wetland and the artificial wetland increase. As a result of landscape fragmentation,ecological function in a landscape was declined. The simulated result by CA-Markov model indicated that cultivated land and artificial land continued to rise, the annual average increase rate of construction land was twice the earlier period and the reducing speed of natural wetland was 56.67% of the earlier stage. Between 1997 and 2018, the three types of land use (cultivated land, natural land and artificial wetland) have all extended to the coastal zone.

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HUA, A. K. "SPATIAL-TEMPORAL ANALYSIS OF PATTERN CHANGES AND PREDICTION IN PENANG ISLAND, MALAYSIA USING LULC AND CA-MARKOV MODEL." Applied Ecology and Environmental Research 16, no. 4 (2018): 4619–35. http://dx.doi.org/10.15666/aeer/1604_46194635.

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Ji, Guangxing, Zhizhu Lai, Haibin Xia, Hao Liu, and Zheng Wang. "Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT." Land 10, no. 4 (April 15, 2021): 421. http://dx.doi.org/10.3390/land10040421.

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The purpose of this paper is to simulate the future runoff change of the Yellow River Basin under the combined effect of land use and climate change based on Cellular automata (CA)-Markov and Soil & Water Assessment Tool (SWAT). The changes in the average runoff, high extreme runoff and intra-annual runoff distribution in the middle of the 21st century are analyzed. The following conclusions are obtained: (1) Compared with the base period (1970–1990), the average runoff of Tangnaihai, Toudaoguai, Sanmenxia and Lijin hydrological stations in the future period (2040–2060) all shows an increasing trend, and the probability of flood disaster also tends to increase; (2) Land use/cover change (LUCC) under the status quo continuation scenario will increase the possibility of future flood disasters; (3) The spring runoff proportion of the four hydrological stations in the future period shows a decreasing trend, which increases the risk of drought in spring. The winter runoff proportion tends to increase; (4) The monthly runoff proportion of the four hydrological stations in the future period tends to decrease in April, May, June, July and October. The monthly runoff proportion tends to increase in January, February, August, September and December.

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Kara, Can, and Naciye Doratlı. "Predict and Simulate Sustainable Urban Growth by Using GIS and MCE Based CA. Case of Famagusta in Northern Cyprus." Sustainability 13, no. 8 (April 16, 2021): 4446. http://dx.doi.org/10.3390/su13084446.

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The research study utilizes Multi Criteria Evaluation (MCE) method in geographic information systems (GIS) environment and uses MCE suitability maps with Cellular Automata (CA) for predicting and simulating sustainable urban development scenarios in Famagusta City. It represents first scenario-based simulations of the future growth of Famagusta as “do-nothing” and “sustainable”. Under the do-nothing scenario, Markov Chain probability analysis with CA models is used with temporal land-use/cover datasets based on the images from 2002 and 2011. It shows that, Famagusta City is moving away from sustainable development. Future expansion of both medium-density and low-density urban zones are always located around existing built-up urban area along transportation lines. A similar model is employed by applying sustainable urban development policies by the policy driven scenario. As a main goal, sustainable urban development includes three main criteria, compactness, environmental protection, and social equity. Additionally, brownfield development, distance from center, soil characteristics, soil productivity, vegetation, environmental protection areas (EPA), distance from local services, distance from open space are used as criteria with Analytical Hierarchy Process (AHP). Having such a simulation with the combination of MCE, GIS, and CA has several advantages. Prediction of urban growth presents possible alternative development in the future; visualization of decision making easier for town planners and supports the spatial planning process; and creates more realistic results of our choices related to urban growth.

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Halmy, Marwa Waseem A., Paul E. Gessler, Jeffrey A. Hicke, and Boshra B. Salem. "Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA." Applied Geography 63 (September 2015): 101–12. http://dx.doi.org/10.1016/j.apgeog.2015.06.015.

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Chu, Lin, Tiancheng Sun, Tianwei Wang, Zhaoxia Li, and Chongfa Cai. "Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA)." Sustainability 10, no. 11 (October 24, 2018): 3854. http://dx.doi.org/10.3390/su10113854.

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The spatial pattern of landscape has great influence on the biodiversity provided by ecosystem. Understanding the impact of landscape pattern dynamics on habitat quality is significant in regional biodiversity conservation, ensuring ecological security guarantee, and maintaining the ecological environmental sustainability. Here, combining CA-Markov and InVEST model, we investigated the evolution of landscape pattern and habitat quality, and presented an explanation for variability of biodiversity linked to landscape pattern in Hubei section of Three Gorges Reservoir Area (TGRA). The spatial-temporal evolution characteristic of landscape pattern from 1990 to 2010 were analyzed by Markov chain. Then, the spatial pattern of habitat quality and its variation in three phases were computed by InVEST model. The driving force for landscape variation was explored by using Logistic regression analysis. Next, the CA-Markov model was used to simulate the future landscape pattern in 2020. Finally, future habitat quality maps were obtained by InVEST model predicted landscape maps. The results concluded that, the overall landscape pattern has changed slightly from 1990 to 2010. Woodland, waters and construction land had the greatest variations in proportion among the landscape types. The area of woodland has been decreasing gradually below the average elevation of 140 m, and the area of waters and construction land increased sharply. Logistics regression results indicated that terrain and climate were the most influencing natural factors compared with human factors. The Kappa coefficient reached 0.92, indicating that CA-Markov model had a good performance in future landscape prediction by adding nighttime light data as restriction factor. The biodiversity has been declining over the past 20 years due to the habitat degradation and landscape pattern variation. Overall, the maximum values of habitat degradation index were 0.1188, 0.1194 and 0.1195 respectively, showing a continuously increasing trend from 1990 to 2010. Main urban areas of Yichang city and its surrounding areas has higher habitat degradation index. The average values of habitat quality index of the whole region were 0.8563, 0.8529 and 0.8515 respectively, showing a continuously decreasing trend. The lower habitat quality index mainly located in the urban land as well as the main and tributary banks of the Yangtze River. Under the business as usual scenario, habitat quality continued to maintain the variation trend of the previous decade, showing a reducing habitat quality index and an increasing area of artificial surface. Under the ecological protection scenario, the variation of habitat quality in this scenario represented reverse trend to the previous decade, exhibiting an increase of habitat quality index and an increasing area of woodland and grassland. Construction of Three Gorges Dam, impoundment of Three Gorges Reservoir (TGR), resettlement of Three Gorges Project and urbanization were the most explanatory driving forces for landscape variation and degradation of habitat quality. The research may be useful for understanding the impact of landscape pattern dynamics on biodiversity, and provide scientific basis for optimizing regional natural environment, as well as effective decision-making support to local government for landscape planning and biodiversity conservation.

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Du, Jin Song, and Xin Bi. "An Adaptive Interacting Multiple Model for Vehicle Target Tracking Method." Advanced Materials Research 718-720 (July 2013): 1286–89. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1286.

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In the field of traffic safety vehicle target tracking prediction as the background, this paper proposes an adaptive interacting multiple model tracking algorithm. According to the field of transportation vehicle movement state characteristics, based on the uniform (CV) and uniformly accelerated motion (CA) model, based on new information structure model of motion of the likelihood function, online adaptive adjustment model of the noise variance and the Markov matrix, realization of maneuvering target movement model and model set adaptation, not only improved IMM algorithm for tracking accuracy, and enhances the real-time performance of system, the simulation results show that, the algorithm for tracking precision compared to the traditional IMM method has bigger improvement.

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Nurhidayati, Ely, Imam Buchori, and Mussadun Mussadun. "PREDIKSI PERKEMBANGAN LAHAN PERMUKIMAN TERHADAP KERENTANAN BENCANA BANJIR DAN KEBAKARAN DI PERMUKIMAN TEPIAN SUNGAI KAPUAS KOTA PONTIANAK." TATALOKA 18, no. 4 (November 29, 2016): 249. http://dx.doi.org/10.14710/tataloka.18.4.249-260.

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Settlements of house on stilts in the Eastern Pontianak is located at the triangle of the Kapuas River and Landak River. This study to determine the changes of settlement’s areas in 2003-2014, predict the settlement’s areas in 2020 and the correlation between the disaster vulnerability and the development of settlement’s areas in the Kapuas riverbanks. This research method integrates quantitative-SIG binary logistic regression and CA-Markov. The data used are Quickbird satellite imagery (2003), elevation data ICONOS (2008) and contour intervals (1 meter). The results are the prediction accuracy (79.74%) and the highest kappa index (0.55). The prediction of settlement’s areas (481.98 hectares) in 2020, shows the highest land expansion in the Parit Mayor Village and the increase of settlement’s areas (6.80 ha/year) in 2014-2020. Regression analysis have a coefficient of 0 in the flooding variable, so the floods did not affected the development of settlement’s areas in the Eastern Pontianak.

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Faichia, Cheechouyang, Zhijun Tong, Jiquan Zhang, Xingpeng Liu, Emmanuel Kazuva, Kashif Ullah, and Bazel Al-Shaibah. "Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos." Sustainability 12, no. 20 (October 13, 2020): 8410. http://dx.doi.org/10.3390/su12208410.

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Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions.

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Li, S. H., B. X. Jin, X. Y. Wei, Y. Y. Jiang, and J. L. Wang. "USING CA-MARKOV MODEL TO MODEL THE SPATIOTEMPORAL CHANGE OF LAND USE/COVER IN FUXIAN LAKE FOR DECISION SUPPORT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 163–68. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-163-2015.

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Spatiotemporal modelling of land use/cover change (LUCC) has become increasingly important in recent years, especially for environmental change and regional planning. There have been many approaches and software packages for modelling LUCC, but developing a model for a specific region is still a difficult task, because it requires large volume of data input and elaborate model adjustment. Fuxian Lake watershed is one of the most important ecological protection area in China and located in southeast of Kunming city, Yunnan province. In this paper, the CA-Markov model is used to analyse the spatiotemporal LUCC and project its course into the future. Specifically, the model uses high resolution remote sensing images of 2006 and 2009 as input data, and then makes prediction for 2014. A quantitative comparison with remote sensing images of 2014 suggests an overall accuracy of 88%. This spatiotemporal modelling method is expected to facilitate the research of many land cover and use applications modelling.

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Khunrattanasiri, Weeraphart. "Comparative Study on CA-Markov Model and CLUE-S Model for Land Use Changed Prediction in National Reserved Forest, Nan Province." Journal of Applied Science 19, no. 2 (December 25, 2020): 87–100. http://dx.doi.org/10.14416/j.appsci.2020.02.008.

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Chotchaiwong and Wijitkosum. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using A CA-Markov Model under Two Different Scenarios." Land 8, no. 9 (September 17, 2019): 140. http://dx.doi.org/10.3390/land8090140.

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This study focused on the prediction of land-use changes in Nakhon Ratchasima city using a CA-Markov Model with GIS. Satellite images taken by Landsat-5 (1992), Landsat-7 (2002) and THEOS (2016) were used to predict land use in 2026. In 1992, the most proportion of land usage was built-up areas (47.76%) and followed by green areas (37.45%), bare lands (13.19%), and water bodies (1.60%), respectively. In 2002, the land use comprised built-up areas (56.04%), green areas (35.52%), bare lands (4.80%) and water bodies (3.63%). By 2016, urbanisation had changed the land use pattern, which comprised built-up areas (70.80%), green areas (20.78%), bare lands (6.37%), and water bodies (2.03%). The data were analysed using a change detection matrix and revealed an increase in built-up area at the expense of all other types, especially green areas. The results were in accordance with the prediction model created in two scenarios. Scenario 1 assumed city expansion following past trends, built-up areas (85.88%), green areas (11.67%), bare lands (2.15%), and water bodies (0.30%). Scenario 2 assumed city expansion in accordance with the national strategy, built-up areas (74.91%), green areas (15.77%), bare lands (8.48%), and water bodies (0.84%). The results indicated an expansion of built-up areas and a shrinking of green areas. In Scenario 2, urban expansion was less than in Scenario 1, and preserving the green area seemed more feasible due to governmental restrictions. The results indicated that planning the urbanisation according to the policies development plans, especially in specific areas, contributed to a more efficient urbanisation growth. The city should provide to promote the use of floor area ratio (FAR) and open space ratio (OSR) with urban planning measures as well as increasing the green areas.

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Septiono, Dony Setiawan, and Mussadun Mussadun. "Model Perubahan Penggunaan Lahan Untuk Mendukung Rencana Pengelolaan Kesatuan Pengelolaan Hutan (Studi Kasus KPH Yogyakarta)." JURNAL PEMBANGUNAN WILAYAH & KOTA 12, no. 3 (December 29, 2016): 277. http://dx.doi.org/10.14710/pwk.v12i3.12903.

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Special Region of Yogyakarta (DIY) experience the dynamics of changes in land use so that the decline in the forest area of the country. The government set the FMU Forest Management Unit as part of efforts to protect the forests remain sustainable so we need a study that could support optimal implementation of the Management Plan Forest Management Unit (FMU RP). One method to support the optimization is to do a land change prediction models. The purpose of this study include: (1) analyze the land use change from 1990 to 2013 period and (2) predicting the year 2023. Changes in land use land studied is 1990 and 2013, which would then be used as a base projection in 2013-2023. Methods to be used are: 1) Analysis of input output, 2) the integration of Markov chain Celullar automata (CA-MC) with logistic regression. The prediction model will use two scenarios, namely: 1) the existing condition of the existing and 2) the assumption of government intervention with the basic rules. The results showed in the period of 1990-2013 there is a change of land use is of 23%, or around 3,703 ha. From the results predicted changes in land use in 2023, with scenario 1 change-forest land dry land agriculture as an area of 1,337 ha and a change of scenario 2 of forest land area of 1264.36 ha.

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Mathanraj, S., N. Rusli, and G. H. T. Ling. "Applicability of the CA-Markov Model in Land-use/Land cover Change Prediction for Urban Sprawling in Batticaloa Municipal Council, Sri Lanka." IOP Conference Series: Earth and Environmental Science 620 (January 9, 2021): 012015. http://dx.doi.org/10.1088/1755-1315/620/1/012015.

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Yang, Jun, Fei Chen, Jianchao Xi, Peng Xie, and Chuang Li. "A Multitarget Land Use Change Simulation Model Based on Cellular Automata and Its Application." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/375389.

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Based on the analysis of the existing land use change simulation model, combined with macroland use change driving factors and microlocal land use competition, and through the application of Python language integrated technical approaches such as CA, GIS, AHP, and Markov, a multitarget land use change simulation model based on cellular automata(CA) is established. This model was applied to conduct scenario simulation of land use/cover change of the Jinzhou New District, based on 1:10000 map scale land use, planning, topography, statistics, and other data collected in the year of 1988, 2003, and 2012. The simulation results indicate the following: (1) this model can simulate the mutual transformation of multiple land use types in a relatively satisfactory way; it takes land use system as a whole and simultaneously takes the land use demand in the macrolevel and the land use suitability in the local scale into account; and (2) the simulation accuracy of the model reaches 72%, presenting higher creditability. The model is capable of providing auxiliary decision-making support for coastal regions with the analysis of the land use change driving mechanism, prediction of land use change tendencies, and establishment of land resource sustainable utilization policies.

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Al-Alola, Seham S., Haya M. Alogayell, Ibtesam I. Alkadi, Soha A. Mohamed, and Ismail Y. Ismail. "Recognition and Prediction of Land Dynamics and Its Associated Impacts in Al-Qurayyat City and along Al-Shamal Train Pathway in Saudi Arabia." Sustainability 13, no. 17 (September 3, 2021): 9913. http://dx.doi.org/10.3390/su13179913.

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Saudi Arabia has experienced substantial urban growth over the last few decades, transforming from rural to urban communities due to rapid economic growth. Saudi Arabia is ranked as one of the most urbanized countries, with more than 80% of its population existing in urban centers. Four Landsat imagery datasets acquired in 1989, 2002, 2013, and 2021 were used to estimate the dynamics of land cover and urban growth in Al-Qurayyat City and investigate the relationship between the construction of Al-Shamal train in 2011 and the land dynamics. The results emphasize a strong intercorrelation between the construction of the Al-Shamal train pathway and the land development and the rapid urbanization in Al-Qurayyat City. The results show that the urban and built-up area expanded from 1.96% to 7.25% between 1989 and 2021. Future prediction of land cover dynamics and urban growth in 2030 were estimated using the Markov chain and CA-Markov models. The findings of future prediction show that more than 60% of the total area of Al-Qurayyat City will transform into urban and built-up areas by 2030. The dramatic increase in urban and built-up areas and the subsequent reduction in other land cover types will impact the environmental sustainability of Al-Qurayyat City. The findings in this paper recommend smart growth, which guarantees environmentally friendly development for future land use/land cover planning in Al-Qurayyat City. This study will be beneficial to the urban planner and policymakers for proper sustainable development decisions by exploring the land cover changing pattern and the trends of urban expansion.

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Aliyu, Yahaya A., Terwase T. Youngu, Aliyu Z. Abubakar, Adamu Bala, and Christianah I. Jesulowo. "Monitoring and forecasting spatio-temporal LULC for Akure rainforest habitat in Nigeria." Reports on Geodesy and Geoinformatics 110, no. 1 (December 1, 2020): 29–38. http://dx.doi.org/10.2478/rgg-2020-0009.

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Abstract For several decades, Nigerian cities have been experiencing a decline in their biodiversity resulting from rapid land use land cover (LULC) changes. Anticipating short/long-term consequences, this study hypothesised the effects of LULC variables in Akure, a developing tropical rainforest city in south-west Nigeria. A differentiated trend of urban LULC was determined over a period covering 1999–2019. The study showed the net change for bare land, built-up area, cultivated land, forest cover and grassland over the two decades to be −292.68 km2, +325.79 km2, +88.65 km2, +8.62 km2 and −131.38 km2, respectively. With a projected population increase of about 46.85%, the study identified that the built-up land cover increased from 1.98% to 48.61%. The change detection analysis revealed an upsurge in built area class. The expansion indicated a significant inverse correlation with the bare land class (50.97% to 8.66%) and grassland class (36.33% to 17.94%) over the study period. The study observed that the land consumption rate (in hectares) steadily increased by 0.00505, 0.00362 and 0.0687, in the year 1999, 2009 and 2019, respectively. This rate of increase is higher than studies conducted in more populated cities. The Cellular Automata (CA) Markovian analysis predicted a 37.92% growth of the study area will be the built-up area in the next two decades (2039). The 20-year prediction for Akure built-up area is within range when compared to CA Markov prediction for other cities across the globe. The findings of this study will guide future planning for rational LULC evaluation.

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Oduola, Wasiu Opeyemi, and Xiangfang Li. "Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System." Cancer Informatics 17 (January 1, 2018): 117693511879026. http://dx.doi.org/10.1177/1176935118790262.

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Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

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Journal articles on the topic 'CA Markov prediction'

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