A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco

2004 ◽  
Vol 29 (8) ◽  
pp. 1407-1418 ◽  
Author(s):  
H. Nfaoui ◽  
H. Essiarab ◽  
A.A.M. Sayigh
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Wind Speed ◽  
Markov Chain Model ◽  
Chain Model ◽  
Stochastic Markov Chain

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 Insight into earthquake sequencing: analysis and interpretation of time-series constructed from the directed graph of the Markov chain model

2015 ◽  
Vol 2 (1) ◽  
pp. 399-424
Author(s):  
M. S. Cavers ◽  
K. Vasudevan
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Directed Graph ◽  
State Transition ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Fano Factor ◽  
Chain Model ◽  
Intrinsic Mode Functions ◽  
Allan Factor

Abstract. Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time-series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time-series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, the orthogonal basis set derived from the time-series using the EEMD, to a detailed analysis to draw information-content of the time-series. Also, we investigate the influence of random-noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behavior. Here, we extend the Fano factor and Allan factor analysis to the time-series of state-to state transition frequencies of a Markov chain. Our results support not only the usefulness the intrinsic mode functions in understanding the time-series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

scholarly journals Stochastic Modeling of Net Radiation conditioned on Wind Speed

2020 ◽  
Vol 3 (2) ◽  
pp. 230-239
Author(s):  
IO Agada ◽  
T Sombo ◽  
EU Utah
Keyword(s):  
Markov Chain ◽  
Wind Speed ◽  
Markov Chain Model ◽  
Daily Maximum ◽  
Chain Model ◽  
International Institute ◽  
Net Radiation ◽  
High Wind ◽  
High Wind Speed ◽  
Low Wind Speed

This study aims at modeling net radiation conditioned on wind speed in Port Harcourt using the stochastic (Markov chain model) approach. Thirty-four (34) years data (1977-2010) on daily maximum and minimum relative humidity, maximum and minimum air temperature, solar irradiance and wind speed were sourced from the International Institute of Tropical Agriculture (IITA) and used in the analysis. A two – state (surplus net radiation conditioned on high wind speed and surplus net radiation conditioned on low wind speed) Markov Chain model was developed and used in the course of this work. The result revealed that net radiation is surplus all through the year and monthly steady state probabilities (long run dependence) of surplus net radiation conditioned on low wind speed dominate all through the year. Further analysis with the model showed that surplus net radiation conditioned on low wind speed would occur for 2.44 days and surplus net radiation conditioned on high wind speed for 1.69 days on the average, resulting to a hot weather and climate

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