Estimations of the Spatial Distributions of Probabilities of the Occurrence of Destructive Earthquakes in the Sichuan Area Based on the Markov Chain–Linear Kriging Coupling Model

2020 ◽  
pp. 2150009
Author(s):  
Xue Yuan ◽  
Hu Dan ◽  
Liu Bin ◽  
Zeng Ling ◽  
Liang Yuan
Keyword(s):  
Spatial Distribution ◽  
Markov Chain ◽  
Natural Disasters ◽  
Markov Chain Model ◽  
Coupling Model ◽  
Standard Errors ◽  
Chain Model ◽  
Spatial Distributions ◽  
Human Beings ◽  
Global Science

An earthquake is one of the most serious natural disasters to human beings. The damage from destructive earthquakes is enormous, and the predictions and estimations of earthquakes are urgent challenges in global science fields. In view of the shortcomings of the Markov chain model and the kriging methods in the estimation of the probabilities of the occurrence of earthquakes, the Markov chain–linear kriging coupling model has been established. The model has been applied to estimate the spatial distribution of probabilities of the occurrence of destructive earthquakes of Ms4.5 and Ms6.0 and above in the Sichuan area. According to the estimations of this model, the maximum probabilities of the occurrence of earthquakes of Ms4.5 and Ms6.0 and above in the Changning area of Yibin are 9.59% and 0.46%, respectively, which are close to the frequencies of occurrences of earthquakes of corresponding magnitude in the series of earthquakes that occurred in June 2019 in the region. The validation indicates that the average standard errors of this model for estimating the probabilities of the occurrence of earthquakes of Ms4.5 and Ms6.0 and above are 4.96% and 0.81%, respectively, which are lower than the probability kriging, and the estimation of this model highlighted the high value region of the probabilities.

scholarly journals Disaster Forecasting Approach In Indonesia: A Fuzzy Time Series - Markov Chain Model

2019 ◽  
Author(s):  
Rahmad Syah
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Natural Disasters ◽  
Markov Chain Model ◽  
Volcanic Eruptions ◽  
Fuzzy Time Series ◽  
Chain Model ◽  
The Past ◽  
The Future ◽  
Transport Accidents

The concept of Fuzzy Time Series to predict things that will happen based on the data in the past, while Markov Chain assist in estimating the changes that may occur in the future. With methods are used to predict the incidence of natural disasters in the future. From the research that has been done, it appears the change, an increase of each disaster, like a tornado reaches 3%, floods reaches 16%, landslides reaches 7%, transport accidents reached 25% and volcanic eruptions as high as 50%.

scholarly journals Aplicação do Modelo Estocástico Cadeia de Markov a Dados Diários de Precipitação dos Estados da Bahia e Sergipe (Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe)

2012 ◽  
Vol 5 (3) ◽  
pp. 509 ◽  
Author(s):  
Winicius Santos Araújo ◽  
Francisco De Assis Salviano de Sousa ◽  
José Ivaldo Barbosa de Brito ◽  
Lourivaldo Mota Lima
Keyword(s):  
Spatial Distribution ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Winter Season ◽  
Coastal Region ◽  
Daily Rainfall ◽  
Summer Season ◽  
High Availability ◽  
Chain Model ◽  
Stochastic Markov Chain

O objetivo desta pesquisa foi fornecer uma distribuição espacial e computar as probabilidades incondicionais e condicionais de primeira ordem das precipitações dos Estados da Bahia e Sergipe. Para tanto, foram utilizados dados diários pluviais referentes a um período de 47 anos (1960-2006) de 75 postos e/ou estações meteorológicas fornecidos pela antiga rede de postos da SUDENE através do DCA. Os resultados mostram que a zona oeste da área pesquisada é a mais favorecida com a precipitação na estação verão, ocorrendo o oposto disto na estação inverno; o leste é o mais beneficiado no inverno, o sul na primavera, e o norte no outono. Foi obtido que a probabilidade incondicional, P(C), na região costeira, é influenciada pela alta disponibilidade de umidade do Atlântico e pela geração de sistemas que provocam precipitação devido ao contraste de temperatura da superfície oceano-continente, particularmente durante os meses de outono e primavera. No verão o efeito oceânico não é percebido devido à alta disponibilidade de umidade sobre a área pesquisada e a alta persistência observada da precipitação diária está associada com os núcleos de máximas precipitações que se destacam na estação verão, enquanto que no inverno a baixa persistência foi predominante.Palavras - chave: distribuição espacial, cadeia de markov, precipitação diária. Application of Stochastic Markov Chain Model to Data Daily Rainfall of the States of Bahia and Sergipe ABSTRACTThe objective of this research was to provide a spatial distribution and compute the probabilities conditional and unconditional first order of precipitation of the States of Bahia and Sergipe. It had been used daily rainfall data relating to a period of 47 years (1960-2006) of 75 stations and/or meteorological stations provided by the former station network SUDENE by DCA. The results show that the area west of the area searched is more favored with rainfall in the summer season, the opposite occurring in this winter season, the east is the most improved in the winter, spring in the south, and north in the autumn. It was obtained that the unconditional probability, P(C), the coastal region, is influenced by the high availability of moisture from the Atlantic and the generation of systems that cause precipitation due to the contrast of surface temperature of ocean-continent, particularly during the autumn months and Spring. In summer the ocean effect is not perceived due to the high availability of moisture over the area surveyed and observed high persistence of daily rainfall is associated with the nuclei of maximum precipitation that stand out in the summer season, while in winter the low persistence prevailed.Keywords: spatial distribution, markov chain, daily precipitation.

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.

Sign in / Sign up

Export Citation Format

Share Document