Non-Homogeneous Hidden Markov Model for Daily Rainfall Amount in Peninsular Malaysia

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Wei Lun Tan ◽  
Fadhilah Yusof ◽  
Zulkifli Yusop
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Monsoon Season ◽  
Daily Rainfall ◽  
Reanalysis Data ◽  
Peninsular Malaysia ◽  
Northeast Monsoon ◽  
Hidden States

The non-homogeneous hidden Markov model (NHMM) generates the rainfall observation depends on few weather states which serve as a link between the large scale atmospheric measures. The daily rainfall at 20 stations from Peninsular Malaysia for 33 years sequences is analyzed using NHMM during the northeast monsoon season. A NHMM with six hidden states are identified. The atmospheric variable was obtained from NCEP Reanalysis Data as predictor. The gridded atmospheric fields are summarized through the principle component analysis (PCA) technique. PCA is applied to sea level pressure (SLP) to identify their principal spatial patterns co-varying with rainfall. The NHMM can accurately simulate the observed daily mean rainfall, correlations between stations for daily rainfall amounts and the quantile-quantile plots. It can be concluded that the NHMM is a useful method to simulate the daily rainfall amounts that may be used to prepare strategies and planning for the unpredicted disaster such as flood and drought.

scholarly journals Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model

2004 ◽  
Vol 17 (22) ◽  
pp. 4407-4424 ◽  
Author(s):  
Andrew W. Robertson ◽  
Sergey Kirshner ◽  
Padhraic Smyth
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Large Scale ◽  
Hidden Markov ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Northeast Brazil ◽  
Rainfall Occurrence ◽  
Dry Spell

Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at 10 gauge stations in the state of Ceará in northeast Brazil during the February–April wet season 1975–2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four “hidden” rainfall states are identified. One pair of the states represents wet-versus-dry conditions at all stations, while a second pair of states represents north–south gradients in rainfall occurrence. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal, interannual, and longer time scales. The first pair of states are shown to be associated with large-scale displacements of the tropical convergence zones, and with teleconnections typical of the El Niño–Southern Oscillation and the North Atlantic Oscillation. A nonhomogeneous HMM (NHMM) is then used to downscale daily precipitation occurrence at the 10 stations, using general circulation model (GCM) simulations of seasonal-mean large-scale precipitation, obtained with historical sea surface temperatures prescribed globally. Interannual variability of the GCM's large-scale precipitation simulation is well correlated with seasonal- and spatial-averaged station rainfall-occurrence data. Simulations from the NHMM are found to be able to reproduce this relationship. The GCM-NHMM simulations are also able to capture quite well interannual changes in daily rainfall occurrence and 10-day dry spell frequencies at some individual stations. It is suggested that the NHMM provides a useful tool (a) to understand the statistics of daily rainfall occurrence at the station level in terms of large-scale atmospheric patterns, and (b) to produce station-scale daily rainfall sequence scenarios for input into crop models, etc.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

scholarly journals Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259670
Author(s):  
Albertas Dvirnas ◽  
Callum Stewart ◽  
Vilhelm Müller ◽  
Santosh Kumar Bikkarolla ◽  
Karolin Frykholm ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Sequence Information ◽  
True Positive ◽  
Dna Barcodes ◽  
Structural Variations ◽  
Genomic Alterations ◽  
Data Set

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.

scholarly journals Forecasting violent events in the Middle East and North Africa using the Hidden Markov Model and regularized autoregressive models

2018 ◽  
Vol 17 (3) ◽  
pp. 269-283
Author(s):  
KSM Tozammel Hossain ◽  
Shuyang Gao ◽  
Brendan Kennedy ◽  
Aram Galstyan ◽  
Prem Natarajan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Lead Times ◽  
External Data ◽  
Two Cities ◽  
Data Capturing ◽  
Action Type ◽  
Statistical Time ◽  
Hidden States

This paper focuses on forecasting Military Action-type events by both state and non-state actors. Here we demonstrate that the dynamics of these types of events can be adequately described by a Hidden Markov Model (HMM) where the hidden states correspond to different operational regimes of an actor, and observations correspond to event frequency—and the HMM effectively predicts events with different lead times. We also demonstrate that one can enrich statistical time series-based methods that work only on historical data by exploiting predictive signals in real-time external data streams. We demonstrate the superior predictive power of the proposed models with evaluation of recent data capturing activities over two groups, ISIS and the Syrian Arab Military, two countries, Syria and Iraq, and two cities, Aleppo and Mosul. We also present an approach to converting predictions of the proposed models to real-world warnings.

scholarly journals Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

2016 ◽  
Author(s):  
Hong Gao ◽  
Hua Tang ◽  
Carlos Bustamante
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

scholarly journals Hidden Markov Model

d'CARTESIAN ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 86 ◽  
Author(s):  
Kezia Tumilaar ◽  
Yohanes Langi ◽  
Altien Rindengan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Learning Problem ◽  
Model Learning ◽  
Observation Sequence ◽  
Evaluation Problem ◽  
Hidden States

Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM)  there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem.  The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved  by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter  to maximize P(O|)  can solved by Baum – Welch algorithm. From description above Hidden Markov Model  with state 3  can describe behavior  from the case studies. Key  words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

scholarly journals Multisite Downscaling of Seasonal Predictions to Daily Rainfall Characteristics over Pacific–Andean River Basins in Ecuador and Peru Using a Nonhomogeneous Hidden Markov Model

2016 ◽  
Vol 17 (2) ◽  
pp. 481-498 ◽  
Author(s):  
Luis E. Pineda ◽  
Patrick Willems
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Hidden Markov ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Rainfall Characteristics ◽  
The Pacific ◽  
Thermally Driven ◽  
Hydrological Prediction

Abstract The seasonal predictability of daily rainfall characteristics is examined over 21 hydrologic units in the Pacific–Andean region of Ecuador and Peru (PAEP) using a nonhomogeneous hidden Markov model (NHMM) and retrospective seasonal information from general circulation models (GCMs). First, a hidden Markov model is used to diagnose four states that play distinct roles in the December–May rainy season. The estimated daily states fall into two wet states, one dry state, and one transitional dry–wet state, and show a systematic seasonal evolution together with intraseasonal and interannual variability. The first wet state represents regionwide wet conditions, while the second one represents north–south gradients. The former could be associated with the annual moisture offshore of the PAEP, thermally driven by the climatological maximum of sea surface temperatures in the Niño-1.2 region. The latter corresponds with the dynamically noisy component of the PAEP rainfall signal, associated with the annual displacement of the intertropical convergence zone. Then, a four-state NHMM is coupled with GCM information to simulate daily sequences at each station. Simulations of the GCM–NHMM approach represent daily rainfall characteristics at station level well. The best skills were found in reproducing the interannual variation of seasonal rainfall amount and mean intensity at the regional-averaged level with correlations equal to 0.60 and 0.64, respectively. At catchment level, the best skills appear over catchments south of 4°S, where hydrologically relevant characteristics are well simulated. It is thus shown that the GCM–NHMM approach provides the potential to produce precipitation information relevant for hydrological prediction in this climate-sensitive region.

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