共查询到20条相似文献，搜索用时 31 毫秒
1.
Sediment deposition and its accumulation in a large resorvoir depends on the inflow and reservoir storage content, respectively. Because of this fact it is possible to model the cumulative deposition of sediment as an additive process defined on a bivariate Markov chain. Using the bivariate Markov chain model the mean and variance of the cumulative deposition of John Martin Reservoir, Colorado, U.S.A. are estimated and compared with observed sedimentation data. 相似文献
2.
A new bivariate Gamma distribution generated from functional scale parameter with application to drought data 总被引：1，自引：0，他引：1
Muhammad Mohsin Albrecht Gebhardt Jürgen Pilz Gunter Spöck 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(5):1039-1054
Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA. 相似文献
3.
Maria Mimikou 《Journal of Hydrology》1984,70(1-4):25-33
Daily precipitation occurrences and their monthly wet-days' sums of precipitation-measuring stations in Greece are modelled with a Markov chain. The order of the chain is taken to be seasonally varying in accordance with the precipitation station's meteorological conditions and geographical location. The modelling efficiency of the Markov chain is significantly improved when it is conjunctively used with a second-order autoregressive stochastic model fitted on the monthly wet-days' sums. 相似文献
4.
《水文科学杂志》2013,58(3)
Abstract Abstract Generating pulses and then converting them into flow are two main steps of daily streamflow generation. Three pulse generation models have been proposed on the basis of Markov chains for the purpose of generating daily intermittent streamflow time series in this study. The first one is based on two two-state Markov chains, whereas the second uses a three-state Markov chain. The third model uses harmonic analysis and fits Fourier series to the three-state Markov chain. Results for a daily intermittent streamflow data series show a good performance of the proposed models. 相似文献
5.
Chuanrong Zhang Weidong Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(2):217-230
Simulating fields of categorical geospatial variables from samples is crucial for many purposes, such as spatial uncertainty
assessment of natural resources distributions. However, effectively simulating complex categorical variables (i.e., multinomial
classes) is difficult because of their nonlinearity and complex interclass relationships. The existing pure Markov chain approach
for simulating multinomial classes has an apparent deficiency—underestimation of small classes, which largely impacts the
usefulness of the approach. The Markov chain random field (MCRF) theory recently proposed supports theoretically sound multi-dimensional
Markov chain models. This paper conducts a comparative study between a MCRF model and the previous Markov chain model for
simulating multinomial classes to demonstrate that the MCRF model effectively solves the small-class underestimation problem.
Simulated results show that the MCRF model fairly produces all classes, generates simulated patterns imitative of the original,
and effectively reproduces input transiograms in realizations. Occurrence probability maps are estimated to visualize the
spatial uncertainty associated with each class and the optimal prediction map. It is concluded that the MCRF model provides
a practically efficient estimator for simulating multinomial classes from grid samples. 相似文献
6.
Modeling the stochastic dependence of air pollution index data 总被引：1，自引：1，他引：0
The air pollution index (API) is a common tool, which is often used for determining the quality of air in the environment. In this study, a discrete-time Markov chain model is applied for describing the stochastic behaviour of API data. The study reported in this paper is conducted based on the data collected from Klang city in Malaysia for a period of 3 years (2012–2014). Based on the API data, we considered a five-state Markov chain for depicting the five different states of the air pollution. We identified the Markov chain is an ergodic Markov chain and determined the limiting distribution for each state of the air pollution. In addition, we have identified the mean first passage time from one state to another. Based on the limiting distribution and the mean return time, we found that the risk of occurrences for unhealthy events is small. However, the risk remains notably troubling. Therefore, the standard of air quality in Klang falls within a margin that is considered healthy for human beings. 相似文献
7.
F. A. Attia P. J. Brockwell 《Stochastic Environmental Research and Risk Assessment (SERRA)》1993,7(3):195-204
We consider an infinite-capacity storage system. The cumulative input to the system is assumed to be either (a) a non-decreasing Lévy process or (b) an integrated continuous-time Markov chain. Reward accumulates at a rate depending on the instantaneous release rate. The objective is to choose the release rule in such a way as to maximize the expected total discounted return. In this note we show how to determine the expected discounted return when the release rate is either constant or a linear function of the content. 相似文献
8.
A. Vinciguerra M. Aleardi A. Hojat M. H. Loke E. Stucchi 《Geophysical Prospecting》2022,70(1):193-209
Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that is usually solved through gradient-descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two-dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high-dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non-linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high-frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un-reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient-based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification. 相似文献
9.
10.
As a basis for development of the annual maximum distribution the so-called partial duration series with Poissonian occurrence times and exponentially distributed peak exceedance values has been selected. The model is generalized by allowing for a Markov dependence between succeeding peak values. Correlation values from p=0 to p=1 can be accounted for by introducing the Marshall-Olkin bivariate exponential distribution, which is presented in detail. The developed distribution function for the annual maximum is throughly analysed and a variety of distribution forms depending on the value of the correlation coefficient and the intensity in the Poisson process is hereby recognized. To a certain extent this might be considered as parallel to the scattering of hydrological regions with different generating mechanisms for the annual maxima. 相似文献
11.
《水文科学杂志》2013,58(3):571-581
Abstract The ability to simulate characteristics of the diurnal cycle of rainfall occurrence, and its evolution over the seasons is important to the forecasting of hydrological impacts resulting from land-use and climate changes within the humid tropics. This stochastic modelling study uses a generalized linear model (GLM) solution to second-order Markov chain models, as these discrete models are better at describing binary occurrence processes on an hourly time-scale than continuous-time approaches such as stochastic state-space models. We show that transition probabilities derived by the Markov chain method need to be time-varying rather than stationary to simulate the evolution of the diurnal cycle of rainfall occurrence over a Southeast Asian monsoon sequence. The conceptual and pragmatic links between discrete diurnal processes and continuous processes occurring over seasonal periods are thereby simulated within the same model. 相似文献
12.
Yeboah Gyasi‐Agyei 《水文研究》2001,15(4):595-608
A hybrid model for point rainfall has been explored to model the diurnal cycles in rainfall properties. The hybrid model is a product of two random processes: an occurrence process and an intensity process. Two occurrence process models, first‐order Markov chain and periodic discrete autoregressive, were compared initially. Fourier series was fitted to the properties of the occurrence and intensity processes of the observed data in order to reduce the number of model parameters. The Bayesian and Akaike information criteria were used to identify the optimum number of harmonics of the Fourier series. Simulation results of the two hybrid models were similar, if not identical, and compared well with the observed. In the average sense, the introduction of diurnal cycles in the model parameters did not improve the reproduction of the observed aggregation properties of the occurrence process. However, the diurnal distributions of the aggregation statistics were significantly improved by increasing the order of the Markov chain model. Also the information criteria tend to favour higher than first‐order Markov chain models. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
13.
Rajib Maity 《水文研究》2012,26(21):3182-3194
In this paper, Split Markov Process (SMP) is developed to assess one‐step‐ahead variation of daily rainfall at a rain gauge station. SMP is an advancement of general Markov Process and specially developed for probabilistic assessment of change in daily rainfall magnitude. The approach is based on a first‐order Markov chain to simulate daily rainfall variation at a point through state/sub‐state transitional probability matrix (TPM). The state/sub‐state TPM is based on the historical transitions from a particular state to a particular sub‐state, which is the basic difference between SMP and general Markov Process. The cumulative state/sub‐state TPM is represented in a contour plot at different probability levels. The developed cumulative state/sub‐state TPM is used to assess the possible range of rainfall in next time step, in a probabilistic sense. Application of SMP is investigated for daily rainfall at four rain gauge stations – Khandwa, Jabalpur, Sambalpur, and Puri, located at various parts in India. There are 99 years of record available out of which approximately 80% of data are used for calibration, and 20% of data are used to assess the performance. Thus, 80 years of daily monsoon rainfall is used to develop the state/sub‐state TPM, and 19 years data are used to investigate its performance. Model performance is assessed in terms of hit rate (HR), false alarm rate (FAR), and percentage captured. It is found that percentage captured is maximum for Khandwa (70%) and minimum for Sambalpur (44%) whereas hit rate is maximum for Sambalpur and minimum for Khandwa (73%). FAR is around 30% or below for Jabalpur, Sambalpur, and Puri. FAR is maximum for Khandwa (37%). Overall, the assessed range, particularly the upper limit, provides a quantification possible extreme value in the next time step, which is a very useful information to tackle the extreme events, such as flooding, water logging and so on. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
14.
1 INTRODUCTION The transport of sediment in rivers with active floodplains is a two-dimensional process because the main channel and the floodplain can have very different transport capacities. Therefore, two-dimensional (2D) models are often used to simulate the streamwise and transverse variations of sediment erosion and deposition. Many 2D numerical models have been presented to simulate sediment transport in floodplains (James, 1985; Pizzuto, 1987; Howard, 1992; Nicholas and Walli… 相似文献
15.
Aurélie Muller Jean-Noël Bacro Michel Lang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(1):33-46
Depth–duration–frequency curves estimate the rainfall intensity patterns for various return periods and rainfall durations.
An empirical model based on the generalized extreme value distribution is presented for hourly maximum rainfall, and improved
by the inclusion of daily maximum rainfall, through the extremal indexes of 24 hourly and daily rainfall data. The model is
then divided into two sub-models for the short and long rainfall durations. Three likelihood formulations are proposed to
model and compare independence or dependence hypotheses between the different durations. Dependence is modelled using the
bivariate extreme logistic distribution. The results are calculated in a Bayesian framework with a Markov Chain Monte Carlo
algorithm. The application to a data series from Marseille shows an improvement of the hourly estimations thanks to the combination
between hourly and daily data in the model. Moreover, results are significantly different with or without dependence hypotheses:
the dependence between 24 and 72 h durations is significant, and the quantile estimates are more severe in the dependence
case. 相似文献
16.
Chu-Chih Chen Meng-Chiuan Shih Kuen-Yuh Wu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(1):21-31
The use of a physiologically based toxicokinetic (PBTK) model to reconstruct chemical exposure using human biomonitoring data,
urinary metabolites in particular, has not been fully explored. In this paper, the trichloroethylene (TCE) exposure dataset
by Fisher et al. (Toxicol Appl Pharm 152:339–359, 1998) was reanalyzed to investigate this new approach. By treating exterior chemical exposure as an unknown model parameter, a
PBTK model was used to estimate exposure and model parameters by measuring the cumulative amount of trichloroethanol glucuronide
(TCOG), a metabolite of TCE, in voided urine and a single blood sample of the study subjects by Markov chain Monte Carlo (MCMC)
simulations. An estimated exterior exposure of 0.532 mg/l successfully reconstructed the true inhalation concentration of
0.538 mg/l with a 95% CI (0.441–0.645) mg/l. Based on the simulation results, a feasible urine sample collection period would
be 12–16 h after TCE exposure, with blood sampling at the end of the exposure period. Given the known metabolic pathway and
exposure duration, the proposed computational procedure provides a simple and reliable method for environmental (occupational)
exposure and PBTK model parameter estimation, which is more feasible than repeated blood sampling. 相似文献
17.
18.
Earthquake occurrence is well-known to be associated with structural changes in underground dynamics, such as stress level and strength of electromagnetic signals. While the causation between earthquake occurrence and underground dynamics remains elusive, the modeling of changes in underground dynamics can provide insights on earthquake occurrence. However, underground dynamics are usually difficult to measure accurately or even unobservable. In order to model and examine the effect of the changes in unobservable underground dynamics on earthquake occurrence, we propose a novel model for earthquake prediction by introducing a latent Markov process to describe the underground dynamics. In particular, the model is capable of predicting the change-in-state of the hidden Markov chain, and thus can predict the time and magnitude of future earthquake occurrences simultaneously. Simulation studies and applications on a real earthquake dataset indicate that the proposed model successfully predicts future earthquake occurrences. Theoretical results, including the stationarity and ergodicity of the proposed model, as well as consistency and asymptotic normality of model parameter estimation, are provided. 相似文献
19.
本文应用回归—马尔可夫链联合预测地震的方法,结合川、滇强震的特点,对川、滇强震进行了计算,并作了预测验证。结果表明,该方法对川、滇强震的预测效果较好。 相似文献
20.
应用马尔可夫链和地震发生率统计关系,建立了鄂尔多斯周边地震带、带间和汾渭地震带各盆地间地震的相对时空转移概率模型,定量地给出了未来2——5年地震带间和盆地间至少发生一次或一次以上不同震级阈地震的相对概率。 相似文献