DEVELOPING AND CALIBRATING A STOCHASTIC RAINFALL GENERATOR MODEL FOR SIMULATING DAILY RAINFALL BY MARKOV CHAIN APPROACH

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
N. S. Dlamini ◽  
M. K. Rowshon ◽  
Ujjwal Sahab ◽  
A. Fikri ◽  
S. H. Lai ◽  
...  
Keyword(s):  
Markov Chain ◽  
Production Systems ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Climate Data ◽  
Management Tools ◽  
Stochastic Weather Generator ◽  
Markov Chain Approach

Rainfall is an important parameter in tropical humid regions for which paddy production systems depend. A significant portion of paddy water requirements is supplied by natural rainfall. Several studies have predicted changes in rainfall patterns and in the amount of rain that may be obtainable in future owing to climate change. There is increased concern about future water availability for an important crop such as rice. Need to develop new water management tools for sustainable production is inevitable, but such tools require long-term climate data that is credible and consistent with the time. This study concerns itself with evaluating a stochastic weather generator (WGEN) model for simulating daily rainfall series. The model is assessed using long-term historical rainfall data obtained from a rice growing irrigation schemes in Malaysia. The model is based on a first-order two-state Markov chain approach which uses two transition probabilities and random number to generate rainfall series. Selected statistical properties were computed for each station and compared against those retrieved from the model after model training and testing. The results obtained from these comparisons are quite satisfactory giving confidence about the performance and future outputs from the model. The model has shown good skill in describing the rainfall occurrence process and rainfall amounts for the area. The model will be adapted in a subsequent study for downscaling and simulating effective daily rainfall series corresponding to future climate scenarios.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach

2020 ◽  
Vol 34 (15) ◽  
pp. 4703-4724
Author(s):  
Saeed Azimi ◽  
Erfan Hassannayebi ◽  
Morteza Boroun ◽  
Mohammad Tahmoures
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Probabilistic Analysis ◽  
Markov Chain Approach

scholarly journals Global evaluation of gridded satellite precipitation products from the NOAA Climate Data Record program

2021 ◽  
Author(s):  
Olivier P. Prat ◽  
Brian R. Nelson ◽  
Elsa Nickl ◽  
Ronald D. Leeper
Keyword(s):  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Probability Of Detection ◽  
Reference Network ◽  
Global Evaluation ◽  
Climate Data ◽  
Satellite Precipitation ◽  
Data Record ◽  
Climate Data Record

AbstractThree satellite gridded daily precipitation datasets: PERSIANN-CDR, GPCP, and CMORPH, that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20-year to over 35-year. The products inter-comparisons are performed at various temporal (daily to annual) and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in-situ data sets from the Global Historical Climatology Network (GHCN-Daily) and the US Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio among others). All SPPs were found to underestimate extreme rainfall (i.e. above the 90th percentile) from about -20% for CMORPH to -50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned; from the long-term qualitative analysis of hydro-climatological properties to the quantification of daily extreme events for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications.

scholarly journals Development and evaluation of a stochastic daily rainfall model with long-term variability

2017 ◽  
Vol 21 (12) ◽  
pp. 6541-6558 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-gamma model with different parameterisations. The key finding is that if the parameters of the gamma distribution are randomly sampled each year from fitted distributions rather than fixed parameters with time, the variability of rainfall depths at both short and longer temporal resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decadally varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Development and Evaluation of a Stochastic Daily Rainfall Model with Long Term Variability

2017 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability, but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov Chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a Gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-Gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-Gamma model with different parameterisations. The key finding is that if the parameters of the Gamma distribution are randomly sampled from fitted distributions prior to simulating the rainfall for each year, the variability of rainfall depths at longer resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decade-varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Labour Market Dynamics in Greek Regions: a Bayesian Markov Chain Approach Using Proportions Data

2010 ◽  
Vol 2 (1) ◽  
pp. 32-45
Author(s):  
George A. Christodoulakis ◽  
Emmanuel C. Mamatzakis
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Labour Market ◽  
Bayesian Approach ◽  
Transition Probabilities ◽  
Full Employment ◽  
Monte Carlo Integration ◽  
Market Dynamics ◽  
Markov Chain Approach ◽  
First Time

This paper focuses on Greek labour market dynamics at a regional base, which comprises of 16 provinces, as defined by NUTS levels 1 and 2 (Eurostat, 2008), using Markov Chains for proportions data for the first time in the literature. We apply a Bayesian approach, which employs a Monte Carlo Integration procedure that uncovers the entire empirical posterior distribution of transition probabilities from full employment to part employment, unemployment and economically unregistered unemployment and vice a versa. Our results show that there are disparities in the transition probabilities across regions, implying that the convergence of the Greek labour market at a regional base is far from being considered as completed. However, some common patterns are observed as regions in the south of the country exhibit similar transition probabilities between different states of the labour market.

scholarly journals Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chulsang Yoo ◽  
Jinwook Lee ◽  
Yonghun Ro
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Rainfall Intensity ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Daily Rainfall ◽  
Monthly Rainfall ◽  
Chain Model ◽  
Climate Change Scenarios ◽  
The Mean

This study evaluates the effect of climate change on daily rainfall, especially on the mean number of wet days and the mean rainfall intensity. Assuming that the mechanism of daily rainfall occurrences follows the first-order Markov chain model, the possible changes in the transition probabilities are estimated by considering the climate change scenarios. Also, the change of the stationary probabilities of wet and dry day occurrences and finally the change in the number of wet days are derived for the comparison of current (1x CO2) and 2x CO2conditions. As a result of this study, the increase or decrease in the mean number of wet days was found to be not enough to explain all of the change in monthly rainfall amounts, so rainfall intensity should also be modified. The application to the Seoul weather station in Korea shows that about 30% of the total change in monthly rainfall amount can be explained by the change in the number of wet days and the remaining 70% by the change in the rainfall intensity. That is, as an effect of climate change, the increase in the rainfall intensity could be more significant than the increase in the wet days and, thus, the risk of flood will be much highly increased.

003 A markov/multi-state model of progression and dynamics of seizure freedom in in large cohort of patients with epilepsy

2018 ◽  
Vol 89 (6) ◽  
pp. A2.3-A3
Author(s):  
Hugh D Simpson ◽  
Zhibin Chen ◽  
Patrick Kwan ◽  
Martin Brodie
Keyword(s):  
Markov Chain ◽  
Natural History ◽  
Transition Probabilities ◽  
Disease Process ◽  
Seizure Freedom ◽  
Cohort Data ◽  
History Of ◽  
State Models ◽  
History Of Epilepsy

IntroductionEpilepsy is a common and often highly morbid disease, associated with a not insignificant mortality. Some aspects of epilepsy have been well studied, for example the genetic basis and phenomenology of certain epilepsy syndromes, while other aspects such as long-term outcomes in large cohorts of epilepsy patients are less well studied. Mathematical modelling can assist in the analysis and interpretation of patterns and trends in long-term cohort data, that might not otherwise be obvious intuitively or using standard statistical methods.MethodsMarkov chain and state based modelling have been successfully used in analysing the progression of stages of disease in other specialties, such as chronic kidney disease and diabetic retinopathy. We applied this methodology to disease progression in epilepsy, by applying it to long-term cohort data. We used data from a longitudinal observational cohort study of 1795 patients with newly diagnosed epilepsy, for up to 30 years. The patients were managed in typical, real world clinical practice.ResultsWe created a framework for conceptualising the natural history of epilepsy as a step-wise progression of disease through various stages, determined by seizure freedom and antiepileptic drug schedule. We adapted mathematical frameworks based on Markov chain and multi-state models to this model. We generated transition probabilities representing the dynamics of moving between stages of disease. This allowed us to analyse the dynamics of progression, and hence prognosis, in one of the largest and longest followed epilepsy cohorts in the world.ConclusionEpilepsy can be conceptualised as multi-stage, stepwise disease process. Markov and multistate models can be used to model the natural history of epilepsy, and inform us of the likely outcomes of epilepsy at the various stages in its progression. This quantification of outcomes can help us better inform patients of their prognosis at various stages in their illness.

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.

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