Drought class transition analysis through different models: a case study in North China

2016 ◽  
Vol 17 (1) ◽  
pp. 138-150 ◽  
Author(s):  
Ting Zhang ◽  
Jianzhu Li ◽  
Rong Hu ◽  
Yixuan Wang ◽  
Ping Feng
Keyword(s):  
Time Series ◽  
Adaptive Filter ◽  
North China ◽  
Markov Chain Model ◽  
Chain Model ◽  
Loglinear Model ◽  
Filter Model ◽  
Drought Prediction ◽  
Drought Classes

The standardized precipitation index (SPI) and standardized runoff index (SRI) are computed for several gauge stations in Panjiakou Reservoir catchment of Luanhe Basin, a drought prone region of North China. Based on the SPI and SRI time series, two different models, a weighted Markov chain model and a Volterra adaptive filter model for chaotic time series, were established to predict drought classes and achieve both short- and long-term drought forecasting. These approaches were compared with a three-dimensional (3D) loglinear model, reported in our previous work. It was observed that all the three models have pros and cons when applied to drought prediction in Panjiakou Reservoir catchment. The 3D loglinear model is able to forecast drought class within 1 month. However, its predicting accuracy declines with the increase of prediction time scale, and this confines its application. The weighted Markov chain model is a useful tool for drought early warning. Its precision, which is significantly related to the stable condition of drought classes, is highest for Non-drought, followed by Moderate and Severe/Extreme drought, and lowest for Near-normal. The Volterra adaptive filter model for chaotic time series combined the phase space reconstruction technique, Volterra series expansion technique and adaptive filter optimization technique, and was for the first time used in a drought class transition study. This model is effective and highly precise in long-term drought prediction (for example, 12 months). It is able to provide reliable information for the medium- and long-term decisions and plans for water resources systems.

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.

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.

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