scholarly journals A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zulfiqar Ali ◽  
Ijaz Hussain ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie ◽  
Muhammad Ismail ◽  
...  
Keyword(s):  
Markov Chain ◽  
Environmental Factors ◽  
Ordinal Data ◽  
Natural Hazard ◽  
Transient Behavior ◽  
Weighting Scheme ◽  
Temperature Index ◽  
Processing Techniques ◽  
Efficient Sequence ◽  
Drought Classes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.

scholarly journals ON THE STABILITY OF A BANDWIDTH PACKING ALGORITHM

2000 ◽  
Vol 14 (1) ◽  
pp. 57-79 ◽  
Author(s):  
Jean-François Dantzer ◽  
Mostafa Haddani ◽  
Philippe Robert
Keyword(s):  
Markov Chain ◽  
Bandwidth Allocation ◽  
Transient Behavior ◽  
Finite Markov Chain ◽  
Countable State Space ◽  
Countable State ◽  
The Stability ◽  
Bandwidth Packing ◽  
Bandwidth Allocation Algorithm ◽  
Transient Markov Chain

The stability properties of the bandwidth allocation algorithm First Fit are analyzed for some distributions on the sizes of the requests. Fluid limits are used to get the ergodicity results. When there are two possible sizes, the description of the transient behavior involves a finite Markov chain on the exit states of a transient Markov chain on a countable state space. The explicit expression of this exit matrix is given.

Process-Dependent Charges and Traps in Dielectrics on SiC

2006 ◽  
Vol 527-529 ◽  
pp. 995-998
Author(s):  
Bharat Krishnan ◽  
Hrishikesh Das ◽  
Yaroslav Koshka ◽  
Igor Sankin ◽  
P.A. Martin ◽  
...  
Keyword(s):  
Charge Injection ◽  
Charge Trapping ◽  
Transient Behavior ◽  
Flat Band ◽  
Interface Traps ◽  
Flat Band Voltage ◽  
Applied Bias ◽  
Voltage Drift ◽  
Charge Stability ◽  
Processing Techniques

Dielectric charges and charge stability were compared in different dielectrics formed on SiC by different processing techniques. The concentration and transient behavior of the interface and trapped charges were investigated. Strong hysteresis and flat-band voltage drift under applied bias were observed in some of the samples. They are attributed to the trapping of the charge injected in the dielectrics. Differences in charge injection, charge trapping, and capture/emission of carriers by interface traps were pronounced for the investigated SiO2 and Si3N4 dielectrics.

scholarly journals FINANCIAL SIGNAL PROCESSING: A SELF CALIBRATING MODEL

2001 ◽  
Vol 04 (04) ◽  
pp. 567-584 ◽  
Author(s):  
ROBERT J. ELLIOTT ◽  
WILLIAM C. HUNTER ◽  
BARBARA M. JAMIESON
Keyword(s):  
Signal Processing ◽  
Markov Chain ◽  
Term Structure ◽  
Markov Models ◽  
Estimation Procedure ◽  
Rate Process ◽  
Treasury Bills ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Selection Of

Previous work on multifactor term structure models has proposed that the short rate process is a function of some unobserved diffusion process. We consider a model in which the short rate process is a function of a Markov chain which represents the "state of the world". This enables us to obtain explicit expressions for the prices of zero-coupon bonds and other securities. Discretizing our model allows the use of signal processing techniques from Hidden Markov Models. This means we can estimate not only the unobserved Markov chain but also the parameters of the model, so the model is self-calibrating. The estimation procedure is tested on a selection of U.S. Treasury bills and bonds.

scholarly journals Quantum fast-forwarding: Markov chains and graph property testing

2019 ◽  
Vol 19 (3&4) ◽  
pp. 181-213 ◽  
Author(s):  
Simon Apers ◽  
Alain Scarlet
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Markov Chains ◽  
Quantum State ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Walk ◽  
Transient Behavior ◽  
Quantum Walks ◽  
Learning Context

We introduce a new tool for quantum algorithms called quantum fast-forwarding (QFF). The tool uses quantum walks as a means to quadratically fast-forward a reversible Markov chain. More specifically, with P the Markov chain transition matrix and D = \sqrt{P\circ P^T} its discriminant matrix (D=P if P is symmetric), we construct a quantum walk algorithm that for any quantum state |v> and integer t returns a quantum state \epsilon-close to the state D^t|v>/\|D^t|v>. The algorithm uses O(|D^t|v>|^{-1}\sqrt{t\log(\epsilon\|D^t|v>})^{-1}}) expected quantum walk steps and O(\|D^t|v>|^{-1}) expected reflections around |v>. This shows that quantum walks can accelerate the transient dynamics of Markov chains, complementing the line of results that proves the acceleration of their limit behavior. We show that this tool leads to speedups on random walk algorithms in a very natural way. Specifically we consider random walk algorithms for testing the graph expansion and clusterability, and show that we can quadratically improve the dependency of the classical property testers on the random walk runtime. Moreover, our quantum algorithm exponentially improves the space complexity of the classical tester to logarithmic. As a subroutine of independent interest, we use QFF for determining whether a given pair of nodes lies in the same cluster or in separate clusters. This solves a robust version of s-t connectivity, relevant in a learning context for classifying objects among a set of examples. The different algorithms crucially rely on the quantum speedup of the transient behavior of random walks.

Changes in mangrove species assemblages and future prediction of the Bangladesh Sundarbans using Markov chain model and cellular automata

2015 ◽  
Vol 17 (6) ◽  
pp. 1111-1117 ◽  
Author(s):  
Anirban Mukhopadhyay ◽  
Parimal Mondal ◽  
Jyotiskona Barik ◽  
S. M. Chowdhury ◽  
Tuhin Ghosh ◽  
...  
Keyword(s):  
Markov Chain ◽  
Environmental Factors ◽  
Cellular Automata ◽  
Markov Chain Model ◽  
Species Assemblages ◽  
Chain Model ◽  
Mangrove Species ◽  
Future Prediction

The composition and assemblage of mangroves in the Bangladesh Sundarbans are changing systematically in response to several environmental factors.

Application of Model Predictive Control to Control Transient Behavior in Stochastic Manufacturing System Models

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Alireza Fazlirad ◽  
Theodor Freiheit
Keyword(s):  
Optimal Control ◽  
Markov Chain ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Transient Behavior ◽  
Research Area ◽  
State Planning ◽  
Expected Performance

Increasing complexity in manufacturing strategies and swift changes in market and consumer requirements have driven recent studies of manufacturing systems, with transient behavior being identified as a key research area. Till date, satisfying consumer demand has focused on steady-state planning of production, mostly using stochastic or deterministic optimal control methods. Due to the difficulty of obtaining optimal control for many practical situations, as well as in evaluating performance under optimal control, these studies have not been conducive to the analysis or control of transient behavior. This paper bridges this gap by applying model predictive control to a manufacturing system modeled as a discrete-time Markov chain. By modifying the initiation of production as probabilities within the Markov chain, a method is proposed to directly control the system to specific expected performance levels and improve its stochastic transient behavior.

scholarly journals Transient Kinetic Proofreading

2021 ◽  
Author(s):  
G. Iyengar ◽  
M. Perry
Keyword(s):  
Markov Chain ◽  
Numerical Experiments ◽  
Markov Chain Model ◽  
Direct Consequence ◽  
Transient Behavior ◽  
Computational Time ◽  
Chain Model ◽  
Stochastic Version ◽  
Wide Range ◽  
Kinetic Proofreading

AbstractWe propose a 2-dimensional Markov chain model to understand and efficiently compute the transient behavior of the kinetic proofreading mechanism in a single T-cell. We show that a stochastic version of absolute ligand discrimination is a direct consequence of the finite number of receptors on the cell surface; thus, pointing to number control as being important for absolute ligand discrimination. We also develop 1-dimensional approximations for several limiting regimes that significantly decrease the computational time. We present results of numerical experiments that explore the behavior of the new model for a wide range of parameters, and its robustness to parameter errors.

scholarly journals Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard

2020 ◽  
Vol 9 (4) ◽  
pp. 268 ◽  
Author(s):  
Annamária Laborczi ◽  
Csaba Bozán ◽  
János Körösparti ◽  
Gábor Szatmári ◽  
Balázs Kajári ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Natural Hazard ◽  
Reference Data ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Excess Water ◽  
Water Hazard ◽  
Water Inundation ◽  
Two Hybrid

Inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Inland excess water is an interrelated natural and human induced land degradation phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behavior of the occurrence, frequency of inland excess water. Spatial auxiliary information representing inland excess water forming environmental factors were taken into account to support the spatial inference of the locally experienced inland excess water frequency observations. Two hybrid spatial prediction approaches were tested to construct reliable maps, namely Regression Kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use, and climate. Comparing the results of the two approaches, we did not find significant differences in their accuracy. Although both methods are appropriate for predicting inland excess water hazard, we suggest the usage of RFK, since (i) it is more suitable for revealing non-linear and more complex relations than RK, (ii) it requires less presupposition on and preprocessing of the applied data, (iii) and keeps the range of the reference data, while RK tends more heavily to smooth the estimations, while (iv) it provides a variable rank, providing explicit information on the importance of the used predictors.

scholarly journals Predicting the probability of transforming different classes of monthly droughts in Iran

2021 ◽  
Author(s):  
Peyman Mahmoudi ◽  
Allahbakhsh Rigi
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Drought Index ◽  
First Passage Time ◽  
Transition Probability ◽  
Severe Drought ◽  
Homogeneous Markov Chain ◽  
Effective Drought Index ◽  
Daily Precipitation Data ◽  
Drought Classes

Abstract The main objective of this study was to predict the transition probability of different drought classes by applying Homogenous and non- Homogenous Markov chain models. The daily precipitation data of 40 synoptic stations in Iran, for a period of 35 years (1983–2018), was used to access the study objectives. The Effective Drought Index (EDI) was applied to categorize Iran’s droughts. With the implementation of cluster analysis on the daily values of effective drought index (EDI), it was observed that Iran can be divided into five separate regions based on the behavior of the time series of the studied stations. The spatial mean of the effective drought index (EDI) of each region was also calculated. After forming the transition frequency matrix, the dependent and correlated test of data was conducted via chi-square test. The results of this test confirmed the assumption that the various drought classes are correlated in five studied regions. Eventually, after adjusting the transition probability matrix for the studied regions, the homogenous and non-homogenous Markov chains were modeled and Markov characteristics of droughts were extracted including various class probabilities of drought severity, the average expected residence time in each drought class, the expected first passage time from various classes of droughts to the wet classes, and the short-term prediction of various drought classes. Regarding these climate areas, the results showed that the probability of each category is reduced as the severity of drought increases from poor drought category to severe and very severe drought. In the non-homogeneous Markov chain, the probability of each category of drought for winter, spring, and fall indicated that the probability of weak drought category is more than other categories. Since the obtained anticipating results are dependent on the early months, they were more accurate than those of the homogeneous Markov chain. In general, both Markov chains showed favorable results that can be very useful for water resource planners.

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