A method for estimating earthquake occurrence probability using first- and multiple-order Markov chain models

1991 ◽  
Vol 4 (1) ◽  
pp. 7-22 ◽  
Author(s):  
Yukio Fujinawa
Keyword(s):  
Markov Chain ◽  
Occurrence Probability ◽  
Earthquake Occurrence ◽  
Markov Chain Models ◽  
Order Markov Chain

Higher-order Markov chain models for categorical data sequences

2004 ◽  
Vol 51 (4) ◽  
pp. 557-574 ◽  
Author(s):  
Wai Ki Ching ◽  
Eric S. Fung ◽  
Michael K. Ng
Keyword(s):  
Markov Chain ◽  
Categorical Data ◽  
Higher Order ◽  
Markov Chain Models ◽  
Order Markov Chain ◽  
Higher Order Markov Chain

scholarly journals Forecasting based on Markov chain modely

2019 ◽  
Vol 34 (01) ◽  
Author(s):  
Ramasubramanian V. ◽  
Ravindra Singh Shekhawat ◽  
S. P. Singh
Keyword(s):  
Markov Chain ◽  
Regression Model ◽  
Crop Yield ◽  
Extreme Values ◽  
Probability Model ◽  
Model Based ◽  
Markov Chain Models ◽  
Markov Chain Theory ◽  
Order Markov Chain ◽  
Chain Theory

In this paper, we try to forecast crop yield by the probability model based on Markov Chain theory, which overcomes some of the drawbacks of the regression model. Markov Chain models are not constrained by a parametric assumption and are robust against outliers and extreme values. Here, multiple order Markov chain were utilized.

First and second order Markov chain models for synthetic generation of wind speed time series

Energy ◽  
2005 ◽  
Vol 30 (5) ◽  
pp. 693-708 ◽  
Author(s):  
A SHAMSHAD ◽  
M BAWADI ◽  
W WANHUSSIN ◽  
T MAJID ◽  
S SANUSI
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Wind Speed ◽  
Second Order ◽  
Markov Chain Models ◽  
Order Markov Chain ◽  
Synthetic Generation

scholarly journals Shallow seismicity in the area of Greece: its character as seen by means of a stochastic model

1995 ◽  
Vol 2 (3/4) ◽  
pp. 136-146 ◽  
Author(s):  
L. C. Polimenakos
Keyword(s):  
Markov Chain ◽  
Large Scale ◽  
Markov Chain Model ◽  
Time Interval ◽  
Chain Model ◽  
Earthquake Occurrence ◽  
Chain Analysis ◽  
Order Markov Chain ◽  
Occurrence Pattern ◽  
Of Greece

Abstract. Occurrence of successive earthquake events in space is analysed by means of semi-stochastic processes. The analysis employs earthquakes events with M > 5.2 from the area of Greece and its surroundings (18-31° E, 34-43° N) for the time interval 1911-1985. The sequence of earthquake occurrences can be only marginally described by a first order Markov chain model. Substitutability analysis incorporates the results of Markov Chains, revealing, though, detailed interrelations of parts (subareas) of the study area, not appreciated in Markov Chain analysis. Reactivation of particular subareas provides an insight into the level of interaction between neighbouring seismogenic sources within a subarea. The earthquake occurrence pattern provides evidence for the effect of a significant stress diffusion through time in the sense of a stress front. Taking into account the limitations of the methodologies applied, results indicate the importance of large-scale monitoring of seismicity, which assist in the identification of particular characteristics of the earthquake occurrence in space and time.

scholarly journals Markov Chain Models for Hydrological Drought Characteristics

2012 ◽  
Vol 13 (1) ◽  
pp. 298-309 ◽  
Author(s):  
Dilek Eren Akyuz ◽  
Mehmetcik Bayazit ◽  
Bihrat Onoz
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Second Order ◽  
Chain Model ◽  
Return Periods ◽  
Annual Streamflow ◽  
Drought Duration ◽  
Drought Characteristics ◽  
Markov Chain Models ◽  
Order Markov Chain

Abstract Estimation of drought characteristics such as probabilities and return periods of droughts of various lengths is of major importance in drought forecast and management and in solving water resources problems related to water quality and navigation. This study aims at applying first- and second-order Markov chain models to dry and wet periods of annual streamflow series to reproduce the stochastic structure of hydrological droughts. Statistical evaluation of drought duration and intensity is usually carried out using runs analysis. First-order Markov chain model (MC1) for dry and wet periods is not adequate when autocorrelation of the original hydrological series is high. A second-order Markov chain model (MC2) is proposed to estimate the probabilities and return periods of droughts. Results of these models are compared with those of a simulation study assuming a lag-1 autoregressive [AR(1)] process widely used to model annual streamflows. Probability distribution and return periods of droughts of various lengths are estimated and compared with the results of MC1 and MC2 models using efficacy evaluation statistics. It is found that the MC2 model in general gives results that are in better agreement with simulation results as compared with the MC1 model. Skewness is found to have little effect on return periods except when autocorrelation is very high. MC1 and MC2 models are applied to droughts observed in some annual streamflow series, with the result that the MC2 model has a relatively good agreement considering the limited duration of the records.

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