scholarly journals Analysis of drought and wet season in Guilan province using SPI relationship and Markov chain model

2021 ◽  
Vol 3 (1) ◽  
pp. 19-30
Author(s):  
Abbas Kashani ◽  
Zahra Arabi ◽  
Gholam Abbas Fallah Ghallhari ◽  
Bromand Salahi
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Drought Index ◽  
Markov Chain Model ◽  
Resource Planning ◽  
Drought Monitoring ◽  
Chain Model ◽  
Wet Season ◽  
Spi Index ◽  
Probability Of Transition

Drought is an urgent environmental issue. Therefore, recognizing its characteristics is of great importance. The most common drought monitoring tool is the Drought Index. The SPI index is a well-known indicator of drought monitoring because of its simplicity, versatility and applicability in any type of climate. In order to investigate the dry matter and estimate its occurrence probability in Rasht city, which is located in the most rainy part of Iran, the time series of SPI were used in combination with Markov chain and the continuity, severity and frequency of dry and wet periods for the period 12.6.3 and 24 months old. The probability of equilibrium in each of the dry and wet conditions and the normal and the expected average expected drought and mildew were predicted, together with the average duration of its duration for different periods. The results showed that the drought share of mild droughts in all periods and droughts Severe is more than 6 months longer. Drought is the highest in the 3rd and 6th month period and is the lowest in the 24-month period. The continuation of drought is greater in the 12 and 24-month intervals, and the probability of transition from one state to another is increased in a longer time series. The probability of equilibrium is more than drought. The SPI profile matches the results of the Markov chain, so combining these two, while improving our ability to evaluate drought monitoring, increases the efficiency of the management system and resource planning.

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 A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT

Kursor ◽  
2019 ◽  
Vol 9 (4) ◽  
Author(s):  
Bagus Dwi Saputra
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Markov Chain Model ◽  
Fish Farming ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Chain Model ◽  
Price Fluctuations

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.

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