Probability Distribution Of Values Research Articles

Scenario Reduction Method based on Output Performance for Condition-based Maintenance Optimization

Condition-based maintenance (CBM) has been widely applied to maintenance policy. Due to the stochastic forecasting degradation, scenario reduction method has been developed to improve the efficiency of CBM. However, most existing scenario reduction methods focus mainly on the input performance of the forecasting degradation without considering the significant output performance characteristic based on the model. In order to warrant the CBM optimization precision while reducing stochastic degradation scenarios efficiently, a new scenario reduction method is formulated that the scenarios with same objective function values can be reduced to one representative scenario. As a result, the reduced scenarios by the proposed method can maintain the probability distributions of objective values, while keeping optimal thresholds close to that of initial scenarios. Finally, the method is applied to select the representative degradation scenarios for CBM optimization model by utilizing vibration-based degradation signals from a rotating machinery application. Compared to the traditional scenario reduction method, the proposed method further improves accuracy and reduction efficiency of CBM optimization.DOI: http://dx.doi.org/10.5755/j01.mech.23.5.15435

Статистический анализ годовых паводков в нижнем течении реки Амур

The statistical analysis of annual floods (annual extremely high levels of floods) in the Amur River’s downstream for all period of instrumental observations from 1896 till 2016 has been carried out. It has been demonstrated, that the general totality of floods extreme levels has the first type of probability distribution for maximum values. Have been defined analytical expressions for probability distribution functions of levels’ general totalities for the period of intensive economic development of the Amur River basin (1961–2016), period of its small economic development (1896–1960), and for all period of water levels’ instrumental observations (1896–2016). Comparison of the Amur River basin’s small and intensive economic development periods has shown that at the intensive economic development risks of large-scale floods on the Amur River exceeding levels of 6.00 and 8.08 meters have decreased approximately two times, and these floods’ average recurrence intervals have increased approximately two times. Thus, intensive economic activity during the period from 1961 till 2016 has improved the flood situation on the Amur River. In addition, the intensive economic development of the Amur River basin has resulted in the most probable water level decrease by 0.5 meter.

A modeling enterprise for chemistry of indoor environments (CIE).

A modeling enterprise for chemistry of indoor environments (CIE).

Risk of natalizumab-associated progressive multifocal leukoencephalopathy in patients with multiple sclerosis: a retrospective analysis of data from four clinical studies

Previous estimates of risk of progressive multifocal leukoencephalopathy (PML) in patients with multiple sclerosis receiving natalizumab were stratified by three risk factors: anti-John Cunningham virus (JCV) antibodies in serum, previous immunosuppressant use, and treatment duration, which were estimated using population-based assumptions. We aimed to calculate PML risk estimates from patient-level risk-factor data and to stratify risk by concentrations of anti-JCV antibody in serum (anti-JCV antibody index). Data on natalizumab-treated patients were pooled from four large, observational, open-label studies: STRATIFY-2, STRATA, TOP, and TYGRIS. Data were analysed with and without imputation for missing values of anti-JCV antibody status and previous immunosuppressant use. For anti-JCV antibody-positive patients in this pooled cohort, cumulative PML risk with or without previous immunosuppressant use was estimated using Kaplan-Meier analysis. Annual PML risks (per 12 natalizumab infusions) for patients without PML in the preceding year were estimated using conditional probability based on the life table method. For anti-JCV antibody-positive patients without previous immunosuppressant use, risk estimates were further stratified using a probability distribution for anti-JCV antibody index values, separately for patients with or without PML. Anti-JCV antibody index cutoffs were selected via sensitivity and specificity assessments for identifying PML cases in an index cohort. 156 (<1%) of 37 249 patients in the pooled cohort had PML. We imputed missing values on anti-JCV antibody status (3912 patients) and on previous immunosuppresant use (544 patients) using a multiple imputation method. For anti-JCV antibody-negative patients (n=13 996), estimated PML risk was less than 0·07 per 1000 patients (95% CI 0·00-0·40). In anti-JCV antibody-positive patients (n=21 696), estimated cumulative PML probability over 6 years (72 infusions of natalizumab) was 2·7% (95% CI 1·8-4·0) in patients with previous immunosuppressant use and 1·7% (1·4-2·1) in those without. In patients without previous immunosuppressant use (n=18 616), estimated annual PML risks per 1000 patients, conditional on having no PML before that year, ranged from 0·01 (0·00-0·03) in year 1 (1-12 infusions) to 0·6 (0·0-1·5) in year 6 (61-72 infusions) for people with an index of 0·9 or less; from 0·1 (0·0-0·2) in year 1 to 3·0 (0·2-5·8) in year 6 for those with an index of more than 0·9 up to and including 1·5; and from 0·2 (0·0-0·5) in year 1 to 10·0 (5·6-14·4) in year 6 for those with an index of more than 1·5. Our risk estimates calculated from patient-level clinical data allow individualised annual prediction of risk of PML in patients receiving natalizumab for multiple sclerosis, supporting yearly benefit-risk re-evaluation in clinical practice. Further, our estimates are generally consistent with previously calculated estimates. Incorporating anti-JCV antibody index allows further risk stratification for anti-JCV antibody-positive patients who have not previously taken immunosuppressants. Biogen.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Square Error Method for threshold estimation in extreme value analysis of wave heights

One of the methods used to model the probability distribution of extreme values is the peaks over threshold method. It requires an appropriate estimation of the threshold above which exceedances are considered to be a sample from a generalized Pareto distribution. Several methods have been proposed to determine the optimal threshold. In this paper, we assess the applicability of four methods developed for hydrological, coastal engineering, financial and stock market purposes, to the case of wave heights from extreme sea conditions due to storms, where significantly less amount of data may be available. Based on the results of this assessment, an improved square error method is formulated, which further allows evaluating the uncertainty in threshold estimation, as well as the uncertainty in the estimation of wave heights for given return periods. The improved method is applied using significant wave height data from hindcast of tropical and extra-tropical storms in the Gulf of Mexico. Results illustrate the capabilities of the proposed method.

EQUAÇÃO DE HARGREAVES-SAMANI CALIBRADA EM DIFERENTES BASES TEMPORAIS PARA SETE LAGOAS, MG

Dados de evapotranspiração de referência (ETo) são requeridos em diversas análises relacionadas ao uso de recursos hídricos, como no manejo de irrigação, balanço hídrico de sequeiro, manejo de bacias hidrográficas, etc. O método FAO Penman-Monteith (FAO-PM) é considerado padrão para cálculo da ETo. Contudo, a aplicação deste método pode ser restringida pela falta de dados meteorológicos. Um dos métodos alternativos com menor requisição de dados é o Hargreaves-Samani (HS), o qual comumente necessita calibração local para obtenção de maior exatidão na aplicação. O objetivo deste trabalho foi avaliar o desempenho do método Hargreaves-Samani, na sua forma original e calibrado em base anual, semestral, trimestral e mensal, frente ao método padrão FAO-PM, com base em extensa série histórica de dados meteorológicos diários (86 anos), para a localidade de Sete Lagoas, MG. Calibraram-se os coeficientes e o expoente da equação de HS pela minimização do erro absoluto médio (EAM). Para avaliar a adequabilidade de aplicação de testes comparativos e para determinar o ajuste das distribuições de probabilidade de ETo, foram aplicados os testes de Kolmogorov-Smirnov e Mann-Whitney, respectivamente. A calibração do método HS anulou a tendência de superestimativa sistemática dos valores de ETo diária, comparativamente ao FAO-PM, reduzindo-se o EAM. A calibração em diferentes bases temporais acarretou a aproximação das distribuições de probabilidade de ETo calculada por meio do método HS, que não diferiram significativamente entre si, à obtida com o método FAO-PM.

Multifractal surrogate-data generation algorithm that preserves pointwise Hölder regularity structure, with initial applications to turbulence.

An algorithm is described that can generate random variants of a time series while preserving the probability distribution of original values and the pointwise Hölder regularity. Thus, it preserves the multifractal properties of the data. Our algorithm is similar in principle to well-known algorithms based on the preservation of the Fourier amplitude spectrum and original values of a time series. However, it is underpinned by a dual-tree complex wavelet transform rather than a Fourier transform. Our method, which we term the iterated amplitude adjusted wavelet transform can be used to generate bootstrapped versions of multifractal data, and because it preserves the pointwise Hölder regularity but not the local Hölder regularity, it can be used to test hypotheses concerning the presence of oscillating singularities in a time series, an important feature of turbulence and econophysics data. Because the locations of the data values are randomized with respect to the multifractal structure, hypotheses about their mutual coupling can be tested, which is important for the velocity-intermittency structure of turbulence and self-regulating processes.

ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit

We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak detections. Next, we calculated the absolute value of the difference between two adjacent RRIs (adRRI), and obtained the empirical probability distributions of adRRI values for valid R-peaks and artifacts. From these, we calculated an optimal threshold for separating adRRI values arising from artifact versus non-artefactual data. We compared the performance of our method with the methods of Berntson and Clifford on the same data. We identified 257,458 R-peak detections, of which 235,644 (91.5%) were true detections and 21,814 (8.5%) arose from artifacts. Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson’s and Clifford’s method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson’s method and 55%, 98%, 96%, 27.5, 0.460 for Clifford’s method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement.

Impact of tropical cyclones on flood risk in southeastern China: Spatial patterns, causes and implications

Using flood peaks data from 62 hydrological stations covering a period of 1951–2014 and tropical cyclone (TC) tracks, floods due to TCs were differentiated and relations between occurrences of floods and TCs were investigated. Then, the impact of TCs on the flood magnitude and frequency was evaluated. Results indicated that there was more than one external influencing factor for flood occurrences, i.e. convective precipitation and TC-related heavy precipitation. In general, the landed TC events have a crucial influence on the occurrence of floods in eastern and western parts of Guangdong province. The mountainous topography limits the northward propagation of TC events and hence limits their impact on the occurrence of floods in the northern part. More than 60% of the largest 10 hazardous floods in the northern and eastern parts were attributed to TCs and hence they significantly modified the probability distributions of flood peaks, increasing the location and scale parameters of the generalized extreme value (GEV) probability distribution model. Therefore, development of measures for mitigation of coastal flooding would need to consider the occurrences of TCs.

Improvement of hydrological model calibration by selecting multiple parameter ranges

Abstract. The parameters of hydrological models are usually calibrated to achieve good performance, owing to the highly non-linear problem of hydrology process modelling. However, parameter calibration efficiency has a direct relation with parameter range. Furthermore, parameter range selection is affected by probability distribution of parameter values, parameter sensitivity, and correlation. A newly proposed method is employed to determine the optimal combination of multi-parameter ranges for improving the calibration of hydrological models. At first, the probability distribution was specified for each parameter of the model based on genetic algorithm (GA) calibration. Then, several ranges were selected for each parameter according to the corresponding probability distribution, and subsequently the optimal range was determined by comparing the model results calibrated with the different selected ranges. Next, parameter correlation and sensibility were evaluated by quantifying two indexes, RC Y, X and SE, which can be used to coordinate with the negatively correlated parameters to specify the optimal combination of ranges of all parameters for calibrating models. It is shown from the investigation that the probability distribution of calibrated values of any particular parameter in a Xinanjiang model approaches a normal or exponential distribution. The multi-parameter optimal range selection method is superior to the single-parameter one for calibrating hydrological models with multiple parameters. The combination of optimal ranges of all parameters is not the optimum inasmuch as some parameters have negative effects on other parameters. The application of the proposed methodology gives rise to an increase of 0.01 in minimum Nash–Sutcliffe efficiency (ENS) compared with that of the pure GA method. The rising of minimum ENS with little change of the maximum may shrink the range of the possible solutions, which can effectively reduce uncertainty of the model performance.

A Fuzzy and a Monte Carlo simulation approach to assess sustainability and rank vehicles in urban environment

Abstract The selection and assessment of new vehicle technologies and mobility solutions to stimulate sustainable transportation planning becomes challenging, due to lack of frameworks and tools that are capable of considering the features of new solutions. The objective of this paper is to present a method that combines Fuzzy Logic and Monte Carlo Simulation (MCS) for the sustainability assessment of urban transportation vehicles and evaluate the applicability of the method to selected indicators for ranking the sustainability performance of vehicles. The fuzzy method has been chosen for its ability to incorporate imprecise and vague information in a decision-making process and the MCS for its ability to generate many scenarios by considering the random sampling of each probability distribution of uncertain input values. The results revealed that by using the fuzzy method alone or with MCS provide similar rankings. The MCS added value to the sustainability assessment by presenting the distribution characteristics of each sustainability index for the five vehicle types and added a layer of statistical confidence in interpreting and comparing the advantages and disadvantages between vehicle types.

Application of Linear Moments and Uncertainty Analysis to Extreme Rainfall Events in Sabah

Linear moments (LM) has been applied in extreme rainfall study for several countries, including China, United States of America, and Peninsular Malaysia. In this study, the LM procedures were applied to extreme rainfall data corresponding to locations provided in Malaysia Urban Stromwater Manual (MSMA) to derive new design rainfalls. Different record lengths were considered to assess the changes in design rainfall, and Monte Carlo simulations were carried out to compute confidence interval of the derived design rainfalls. Based on the Goodness-of-Fit (GoF) test results, the Generalized Extreme Value (GEV) probability distribution was chosen to derive the design rainfalls. The updated design rainfalls at all four locations showed significant reduction at design rainfalls of 50-year ARI and above. The difference of the design rainfalls from shorter record lengths with respect to the full record length and the confidence intervals do not necessarily reduce with a longer record. In hypothetical cases where 100-yr ARI rainfall was added, the increase in design rainfalls did not exceed the upper bound of the confidence intervals. The derived confidence intervals hence allow for better risk assessment, and should be considered in the design of critical structures, i.e. dams.

Residual slip of sliding blocks induced by near‐fault ground motions

SummaryThe response of a rigid block supported on a horizontally moving foundation through a dry‐friction contact is investigated to near‐fault ground motions. Such motions can be thought of as consisting of a coherent component (‘pulse’) and an incoherent component, which can be described as a band‐limited ‘random noise’. The equation of motion of this strongly nonlinear system is reduced to a normalized form that reveals important parameters of the problem such as the critical acceleration ratio. The response of the sliding block to a set of uniformly processed near‐fault motions, covering a sufficiently wide range of magnitudes, is evaluated numerically for selected discrete values of the acceleration ratio. For each value of the critical acceleration ratio, the numerically computed residual slips are fitted with a Weibull (Gumbel type III) extreme value probability distribution. This allows the establishment of regression equations that describe accurately design sliding curves corresponding to various levels of non‐exceedance probability. The analysis reveals that the coherent component of motion contributes significantly to the response of the sliding block. Furthermore, the relevant acceleration in specifying the critical acceleration ratio is the (normalized) amplitude, αH_pulse, of the pulse and not the (normalized) amplitude of the incoherent component αH. Finally, the incoherent component is described quantitatively in terms of the root‐mean‐square acceleration aRMS, and an attempt is made to understand its influence on the response of the sliding block. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Adhesive Performance and Clinician Satisfaction of Two Novel Chlorhexidine Gluconate-impregnated Vascular Access Dressings in Comparison to a Commercial Reference

Design rainfall statistics are the primary inputs used to assess flood risk across river catchments. These statistics normally take the form of Intensity–Duration–Frequency (IDF) curves that are derived from extreme value probability distributions fitted to observed daily, and sub-daily, rainfall data. The design rainfall relationships are often required for catchments where there are limited rainfall records, particularly catchments in remote areas with high topographic relief and hence some form of interpolation is required to provide estimates in these areas. This paper assesses the topographic dependence of rainfall extremes by using elevation-dependent thin plate smoothing splines to interpolate the mean annual maximum rainfall, for periods from one to seven days, across Australia. The analyses confirm the important impact of topography in explaining the spatial patterns of these extreme rainfall statistics. Continent-wide residual and cross validation statistics are used to demonstrate the 100-fold impact of elevation in relation to horizontal coordinates in explaining the spatial patterns, consistent with previous rainfall scaling studies and observational evidence. The impact of the complexity of the fitted spline surfaces, as defined by the number of knots, and the impact of applying variance stabilising transformations to the data, were also assessed. It was found that a relatively large number of 3570 knots, suitably chosen from 8619 gauge locations, was required to minimise the summary error statistics. Square root and log data transformations were found to deliver marginally superior continent-wide cross validation statistics, in comparison to applying no data transformation, but detailed assessments of residuals in complex high rainfall regions with high topographic relief showed that no data transformation gave superior performance in these regions. These results are consistent with the understanding that in areas with modest topographic relief, as for most of the Australian continent, extreme rainfall is closely aligned with elevation, but in areas with high topographic relief the impacts of topography on rainfall extremes are more complex. The interpolated extreme rainfall statistics, using no data transformation, have been used by the Australian Bureau of Meteorology to produce new IDF data for the Australian continent. The comprehensive methods presented for the evaluation of gridded design rainfall statistics will be useful for similar studies, in particular the importance of balancing the need for a continentally-optimum solution that maintains sufficient definition at the local scale.

Attributing seasonal variation of daily extreme precipitation events across The Netherlands

A recent study showed a rise in total and extreme precipitation in the Netherlands over the past century. The present study attempts to characterize and attribute the seasonal variation of daily extreme precipitation events in the Netherlands. Statistical models for extreme values were used to fit daily rainfall maxima for all months during the period 1961–2014, using data from the 231 rain gauges distributed across the country. A generalized extreme value (GEV) approach was used to determine the probability distribution of extreme values and their dependency on time and the monthly North Atlantic Oscillation (NAO) index. The non-stationary models used to represent the annual cycle of the GEV parameters assumed an invariant shape parameter and harmonic functions as location and scale parameters. The best non-stationary model was selected using Akaike’s information criterion (AIC) and the log-likelihood ratio test (LRT). The results indicated that the estimates derived from the non-stationary model differed from those obtained with the aid of the stationary model, and had lower uncertainties. These non-stationary estimates were within the confidence intervals (CI) of the stationary estimates at most rain gauge stations. The non-stationary model estimated parameters with less uncertainty and with smaller CI, thus permitting more accurate representation of extreme precipitation in the Netherlands. The spatial pattern of annual mean location and scale GEV parameters was compatible with coastal, land cover (such as the wooded and heathland areas of the Veluwe region of the province of Gelderland) and orography (in the southeast of the country). The location parameter peaked over the west coast, especially on the central west coast during the summer half-year, while the centre and east of the country had the highest values during the winter half-year. The scale parameter peaked in the centre of the country during the summer, in the east in the early summer and along the west coast in the spring. The 10-year and 50-year return levels were calculated with the aid of the non-stationary model for all months. The spatial distribution of these extreme event probability clearly reflects the regional differences in the Netherlands.

The definition of the thermodynamic entropy in statistical mechanics

A definition of the thermodynamic entropy based on the time-dependent probability distribution of the macroscopic variables is developed. When a constraint in a composite system is released, the probability distribution for the new equilibrium values goes to a narrow peak. Defining the entropy by the logarithm of the probability distribution automatically makes it a maximum at the equilibrium values, so it satisfies the Second Law. It is also satisfies the postulates of thermodynamics. Objections to this definition by Dieks and Peters are discussed and resolved.

Flood quantiles scaling with upper soil hydraulic properties for different land uses at catchment scale

Changes in land use within a catchment are among the causes of non-stationarity in the flood regime, as they modify the upper soil physical structure and its runoff production capacity. This paper analyzes the relation between the variation of the upper soil hydraulic properties due to changes in land use and its effect on the magnitude of peak flows: (1) incorporating fractal scaling properties to relate the effect of the static storage capacity (the sum of capillary water storage capacity in the root zone, canopy interception and surface puddles) and the upper soil vertical saturated hydraulic conductivity on the flood regime; (2) describing the effect of the spatial organization of the upper soil hydraulic properties at catchment scale; (3) examining the scale properties in the parameters of the Generalized Extreme Value (GEV) probability distribution function, in relation to the upper soil hydraulic properties. This study considered the historical changes of land use in the Combeima River catchment in South America, between 1991 and 2007, using distributed hydrological modeling of daily discharges to describe the hydrological response. Through simulation of land cover scenarios, it was demonstrated that it is possible to quantify the magnitude of peak flows in scenarios of land cover changes through its Wide-Sense Simple Scaling with the upper soil hydraulic properties.

Method of studying corrosion processes of metal electrodes by surface voltage fluctuations

The way of study of corrosion processes on the surface of the metal electrode based on the results of observations of voltage fluctuations on it is proposed. The signal processing device for voltage measured on the surface of the metal electrode is developed. The device comprises a differential amplifier, bandpass filter and amplifier with controllable gain. The method of testing of the developed device is described. The research of the electrical noise voltage of the steel electrode immersed in an electrolyte solution is performed. The occurrence of the spectral component at the frequency of 10–12Hz during a series of experiments was observed. The searchfor sustainable criteria for evaluating the presence of corrosion processes in the studied surface area revealed that the most informative parameter in the study of voltage fluctuations associated with corrosion processes on the surface of the metal electrode is the shape of probability distribution of instantaneous values of voltage and evaluation of information entropy. The shape of the probability distribution can be a source of additional information on the progress of electrochemical reactions on the surface because dry electrode signal distribution law was similar in shape to normal. Changing the shape of this distribution with the introduction of additional local maximum shows the influence of extraneous input sources, which can be an electrochemical reaction. Information entropy estimation of instantaneous signal values at lower settlement expenses made it possible to distinguish between the results of different experiments. The changing information entropy of instantaneous values , means the changed properties of the sources of electrical signals on the surface of the investigated electrode. The research enables to carry out external monitoring of corrosion processes on the inner surface of metal pipes.

Ionospheric Spatial Gradient Detector Based on GLRT Using GNSS Observations

A space-based augmentation system (SBAS) provides delay corrections to the Global Navigation Satellite System (GNSS) along with the residual error bounds falling within a high confidence interval. SBAS considerably improves the safety of an aircraft during flight even under all weather conditions. The dispersive nature of the ionosphere is the largest contributor of range error in GNSS, thus threatening its accuracy. Scrutiny of the ionospheric behavior over low latitudes is one of the most challenging tasks for any SBAS system. In this letter, an attempt has been made to detect the spatial gradients in the ionospheric vertical total electron content (VTEC), using statistical hypothesis tests, for the chosen probability of false alarm ( $P_{fa}$ ) and probability of detection ( $P_{d}$ ). Logarithm of likelihood ratio test (log LRT) and generalized likelihood ratio test (GLRT) were performed individually on GNSS data recorded by the multifrequency receiver at Koneru Lakshmaiah University, Guntur (GNT), Andhra Pradesh, India. The tests were performed on the data recorded on January 22, 2013, and March 17, 2013, which were geomagnetically quiet and disturbed days, respectively. The tests were validated by selectively introducing external noise in the VTEC and observing the outcomes. GLRT not only demonstrated a superior performance over log LRT, but it was also simpler to implement as it did not require any prior knowledge of the probability distribution of TEC values.