Detection and Prediction of Urban Expansion of Hanoi Area (Vietnam) Using SPOT-5 Satellite Imagery and Markov Chain Model

2017 ◽  
pp. 119-133
Author(s):  
Trung Van Nguyen ◽  
Nam Van Nguyen ◽  
Ha Thu Thi Le ◽  
Hien Phu La ◽  
Dieu Tien Bui
Keyword(s):  
Markov Chain ◽  
Satellite Imagery ◽  
Markov Chain Model ◽  
Urban Expansion ◽  
Chain Model ◽  
Spot 5

scholarly journals Modeling Urban Encroachment on the Agricultural Land of the Eastern Nile Delta Using Remote Sensing and a GIS-Based Markov Chain Model

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 114 ◽  
Author(s):  
Kelsee Bratley ◽  
Eman Ghoneim
Keyword(s):  
At Risk ◽  
Markov Chain ◽  
Urban Areas ◽  
Agricultural Land ◽  
Nile Delta ◽  
Markov Chain Model ◽  
Urban Expansion ◽  
Chain Model ◽  
Agricultural Lands ◽  
Area At Risk

Historically, the Nile Delta has played an integral part in Egyptian civilization, as its fertile soils have been cultivated for centuries. The region offers a lush oasis among the expansive arid climate of Northern Africa; however, in recent decades, many anthropogenic changes to the environment have jeopardized Egypt’s agricultural productivity. Political instability and lack of sufficient regulations regarding urban growth and encroachment have put agricultural land in the area at risk. Advanced geospatial techniques were used to assess the rate at which urban areas are increasing within the region. A hybrid classification of Landsat satellite imagery for the eastern sector of the Nile Delta, between the years 1988 and 2017, was conducted to map major land-use and land-cover (LULC) classes. The statistical change analysis revealed that urban areas increased by 222.5% over the study period (29 years). Results indicated that urban areas are encroaching mainly on established agricultural lands within the Nile Delta. Most of the change has occurred within the past nine years, where approximately 235.60 km2 of the cultivated lands were transitioned to urban. Nonetheless, at the eastern delta flank, which is bordered by desert, analysis indicated that agricultural lands have experienced a considerable growth throughout the study period due to a major desert reclamation effort. Areas most at risk from future urban expansion were identified. A simulation of future urban expansion, using a Markov Chain algorithm, indicated that the extent to which urban area is simulated to grow in the region is 16.67% (277.3 km2) and 37.82% (843 km2) by the year 2026, and 2050, respectively. The methods used in this study are useful in assessing the rate of urban encroachment on agricultural lands and can be applied to similar at-risk areas in the regions if appropriate site-specific modifications are considered.

scholarly journals Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria

2021 ◽  
Vol 18 (1) ◽  
pp. 30-38
Author(s):  
P.A. Adegbola ◽  
J.R. Adewumi ◽  
O.A. Obiora-Okeke
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Use Change ◽  
Land Cover ◽  
Markov Chain Model ◽  
Urban Expansion ◽  
Efficiency Index ◽  
Landsat 8 ◽  
Chain Model ◽  
Negative Consequences

Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence. Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanization

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Evaluating basketball player’s rotation line-ups performance via statistical markov chain modelling

2021 ◽  
pp. 174795412110090
Author(s):  
Pavlos Kolias ◽  
Nikolaos Stavropoulos ◽  
Alexandra Papadopoulou ◽  
Theodoros Kostakidis
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Specific Rotation ◽  
Multiple Regression Model ◽  
Chain Model ◽  
Know How ◽  
Basketball Game ◽  
Long Run ◽  
Rotation Line ◽  
Rating Category

Coaches in basketball often need to know how specific rotation line-ups perform in either offense or defense and choose the most efficient formation, according to their specific needs. In this research, a sample of 1131 ball possession phases of Greek Basket League was utilized, in order to estimate the offensive and defensive performance of each formation. Offensive and defensive ratings for each formation were calculated as a function of points scored or received, respectively, over possessions, where possessions were estimated using a multiple regression model. Furthermore, a Markov chain model was implemented to estimate the probabilities of the associated formation’s performance in the long run. The model could allow us to distinguish between overperforming and underperforming formations and revealed the probabilities over the evolution of the game, for each formation to be in a specific rating category. The results indicated that the most dominant formation, in terms of offense, is Point Guard-Point Guard-Small Forward-Power Forward-Center, while defensively schema Point Guard-Shooting Guard-Small Forward-Center-Center had the highest rating. Such results provide information, which could operate as a supplementary tool for the coach’s decisions, related to which rotation line-up patterns are mostly suitable during a basketball game.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

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