The life and work of A.A. Markov

Башарин, Г. П.; Langville, Amy N.; Naumov, Valeriy

doi:10.1016/j.laa.2003.12.041

Cited by 91 publications

(34 citation statements)

References 7 publications

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“…The choice of a right simulation technique/model depends upon individual context (e.g., the processes of land transformation), availability of datasets, objective of the research, and the accuracy of the prediction [20,51]. In this research, the combination of Markov Chain [59] and MLP modeling techniques [60] were applied to project the future land cover patterns due to their accuracy in outcomes and wide acceptability [61,62]. The MLP neural network model takes into account the rate of changes (e.g., expansion) in the built-up areas at the expense of other land cover types (e.g., in this case water body, vegetation, and bare soil).…”

Section: Simulating Land Cover Maps For 2019 and 2029mentioning

confidence: 99%

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

et al. 2013

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Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30 °C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of

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Section: Simulating Land Cover Maps For 2019 and 2029mentioning

confidence: 99%

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

et al. 2013

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“…The first model that has been implemented is given the name as "Stochastic Markov Model (St_Markov)", because this model combines both the stochastic processes as well Markov chain analysis techniques [17]. This kind of predictive land cover change modeling is appropriate when the past trend of land cover changing pattern is known [12].…”

Section: Stochastic Markov Modelmentioning

confidence: 99%

“…The number of hidden layer nodes is estimated by the following equation [12]: (17) where, N h = the number of hidden nodes; N i = the number of input nodes; N o = the number of output nodes…”

Section: Number Of Nodesmentioning

confidence: 99%

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Ahmed¹,

Ahmed

2012

IJGI

217

130

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Abstract:The primary objective of this research is to predict and analyze the future urban growth of Dhaka City using the Landsat satellite images of 1989, 1999 and its surrounding impact areas have been selected as the study area. At the beginning, a fisher supervised classification method has been applied to prepare the base maps with five land cover classes. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These have been named as "Stochastic Markov (St_Markov)" Model, "Cellular Automata Markov (CA_Markov)" Model and "Multi Layer Perceptron Markov (MLP_Markov)" Model. Then the best-fitted model has been selected by implementing a method to compare land cover categories in three maps: a reference map of time 1, a reference map of time 2 and a simulation map of time 2. This is how the "Multi Layer Perceptron Markov (MLP_Markov)" Model has been qualified as the most appropriate model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model extrapolates that built-up area increases from 46% to 58% of the total study area during 2009-2019.

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“…For this purpose, the base maps of 1989 and 1999 are used in all three cases. The first model that has been implemented is given the name as -Stochastic Markov Model (St_Markov)‖ [30], because this model combines both the Stochastic processes as well Markov Chain analysis techniques [31,32]. The second model is termed as -Cellular Automata Markov Model (CA_Markov)‖ [30].…”

Section: Simulating Land Cover Mapsmentioning

confidence: 99%

Evaluation of Model Validation Techniques in Land Cover Dynamics

Ahmed

Zhu

2013

IJGI

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This paper applies different methods of map comparison to quantify the characteristics of three different land change models. The land change models used for simulation are termed as -Stochastic Markov (St_Markov)‖, -Cellular Automata Markov (CA_Markov)‖ and -Multi Layer Perceptron Markov (MLP_Markov)‖ models. Various model validation techniques such as per category method, kappa statistics, components of agreement and disagreement, three map comparison and fuzzy methods have then been applied. A comparative analysis of the validation techniques has also been discussed. In all cases, it is found that -MLP_Markov‖ gives the best results among the three modeling techniques. Fuzzy set theory is the method that seems best able to distinguish areas of minor spatial errors from major spatial errors. Based on the outcome of this paper, it is recommended that scientists should try to use the Kappa, three map comparison and fuzzy methods for model validation. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of model validation techniques and articulates priorities for future research.

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The life and work of A.A. Markov

Cited by 91 publications

References 7 publications

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Evaluation of Model Validation Techniques in Land Cover Dynamics

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