STATISTICAL ANALYSIS FOR TERENGGANU FORWARD SCATTER RADAR SEASIDE CLUTTER

2016 ◽  
Vol 78 (5-7) ◽  
Author(s):  
Nor Najwa Ismail ◽  
Nur Emileen Abd Rashid ◽  
Zuhani Ismail Khan
Keyword(s):  
Statistical Analysis ◽  
Weibull Distribution ◽  
High Frequency ◽  
Goodness Of Fit ◽  
Distribution Model ◽  
Logistic Distribution ◽  
Distribution Models ◽  
Very High Frequency ◽  
Forward Scatter ◽  
Scatter Radar

The statistical analysis for Terengganu, Malaysia seaside clutter is presented in this paper. The measured clutter data were collected using a prototype of forward scatter radar (FSR) micro-sensor network with very high frequency (VHF) and ultra-high frequency (UHF) bands. Four categories of clutter strength were recorded during the measurements, which are low, medium, strong and very strong clutter. The classes were divided according to the wind speed occurred during the measurements period. The analysis is to determine the best-fit distribution model for the measured clutter data. Four types of distribution models are used in this analysis, which are Weibull, Gamma, Log-Logistic and Log-Normal distribution. One of the goodness of fit (GOF) tests called root mean square error (RMSE) is used to prove which distribution is a better fit to the probability distribution of the measured clutter data. The obtained results show that for 64 MHz with all clutter level strength, Weibull distribution provides better fit and records the lowest RMSE. Weibull distribution also fits best to the clutter data for low clutter of 151 MHz. However, for the rest of clutter level strength for 151 MHz, Gamma distribution is the best-fitted model with lowest RMSE values. Log-Logistic distribution proves to be the best fitted model to all clutter level strength of clutter data for 434 MHz with smallest RMSE values.

scholarly journals Clutter Distribution Analysis of A Tropical Foliage Clutter for Different Profiles Based on FSR Micro-Sensor Network

2018 ◽  
Vol 7 (3.7) ◽  
pp. 72
Author(s):  
Nur Alia Zulkifli ◽  
N E. Abd Rashid ◽  
Z I. Khan ◽  
N N. Ismail ◽  
R S. A. Raja Abdullah ◽  
...  
Keyword(s):  
High Frequency ◽  
Free Space ◽  
Distribution Model ◽  
Distribution Analysis ◽  
Distribution Models ◽  
Very High Frequency ◽  
Forward Scatter ◽  
Micro Sensor ◽  
Log Normal ◽  
Very High

Comparison of four different clutter profiles (border, seaside, free space and forest) using Forward Scatter Radar (FSR), which operates in Ultra-High and Very High Frequency (UHF and VHF) bands, is analyzed in this paper. Clutter levels ranging from low, medium, strong and very strong on each profile were studied. Based on the standard deviation of each clutter profile, border suits the best profile as the strongest clutter profile amidst seaside and free space, while the forest is determined as the lowest clutter profile. Subsequently, the characteristics of the clutter are investigated and compared based on the five distribution models (Log-Normal, Log-Logistic, Gamma, Weibull and Nakagami).  The parameters of the five distributions are evaluated using Root Mean Square Error (RMSE) in order to prove that the distribution model fits best to the clutter data. It can be concluded that Gamma distribution is the best distribution model for all cases of frequency bands and profiles.  

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

scholarly journals Best Fitted Distribution For Meteorological Data In Kuala Krai

2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Siti Mariam Norrulashikin ◽  
Fadhilah Yusof ◽  
Siti Rohani Mohd Nor ◽  
Nur Arina Bazilah Kamisan
Keyword(s):  
Climate Change ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Meteorological Data ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Data Series ◽  
Distribution Models ◽  
Goodness Of Fit Tests

Modeling meteorological variables is a vital aspect of climate change studies. Awareness of the frequency and magnitude of climate change is a critical concern for mitigating the risks associated with climate change. Probability distribution models are valuable tools for a frequency study of climate variables since it measures how the probability distribution able to fit well in the data series. Monthly meteorological data including average temperature, wind speed, and rainfall were analyzed in order to determine the most suited probability distribution model for Kuala Krai district. The probability distributions that were used in the analysis were Beta, Burr, Gamma, Lognormal, and Weibull distributions. To estimate the parameters for each distribution, the maximum likelihood estimate (MLE) was employed. Goodness-of-fit tests such as the Kolmogorov-Smirnov, and Anderson-Darling tests were conducted to assess the best suited model, and the test's reliability. Results from statistical studies indicate that Burr distributions better characterize the meteorological data of our research. The graph of probability density function, cumulative distribution function as well as Q-Q plot are presented.

scholarly journals Flood Flow Probability Distribution Model Selection on Niger/Benue River Basins in Nigeria

2021 ◽  
pp. 76-94
Author(s):  
Itolima Ologhadien
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
River Basins ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Distribution Models ◽  
Type Iii ◽  
Pearson Type ◽  
Best Fit

Flood frequency analysis is a crucial component of flood risk management which seeks to establish a quantile relationship between peak discharges and their exceedance (or non-exceedance) probabilities, for planning, design and management of infrastructure in river basins. This paper evaluates the performance of five probability distribution models using the method of moments for parameter estimation, with five GoF-tests and Q-Q plots for selection of best –fit- distribution. The probability distributions models employed are; Gumbel (EV1), 2-parameter lognormal (LN2), log Pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value( GEV). The five statistical goodness – of – fit tests, namely; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR) were used to identify the most suitable distribution models. The study was conducted using annual maximum series of nine gauge stations in both Benue and Niger River Basins in Nigeria. The study reveals that GEV was the best – fit distribution in six gauging stations, LP3 was best – fit distribution in two gauging stations, and PR3 is best- fit distribution in one gauging station. This study has provided a significant contribution to knowledge in the choice of distribution models for predicting extreme hydrological events for design of water infrastructure in Nigeria. It is recommended that GEV, PR3 and LP3 should be considered in the development of regional flood frequency using the existing hydrological map of Nigeria.

Reliability Evaluation on Machining Center Based on Three-Parameter Weibull Distribution

2012 ◽  
Vol 430-432 ◽  
pp. 1645-1649 ◽  
Author(s):  
Gong Chang Ren ◽  
Zhi Wei Yang ◽  
Bo Min Meng
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Location Parameter ◽  
Least Square Method ◽  
Optimization Method ◽  
Reliability Evaluation ◽  
Least Square ◽  
Distribution Model ◽  
Adaptive Genetic Algorithm ◽  
Machining Center

In order to improve the model accuracy of reliability evaluation, the Three-Parameter Weibull Distribution model of time between fault was established by introducing location parameters. The correlation coefficient optimization method based on the adaptive genetic algorithm was firstly applied to estimate the location parameter of the Three-Parameter Weibull Distribution. Shape parameter and scale parameter were obtained by the least square method. The time between failures of these series machining center submitting to three-parameter weibull Distribution was checked by the test hypothesis of goodness-of-fit distribution. Finally, the machining center was carried out reliability evaluation based on the Three-Parameter Weibull Distribution model.

A Disturbing Factor in Very High Frequency Communications Via Ionospheric Forward Scatter

Nature ◽  
1956 ◽  
Vol 178 (4545) ◽  
pp. 1280-1283 ◽  
Author(s):  
D. A. CROW ◽  
F. A. KITCHEN ◽  
G. A. ISTED ◽  
G. MILLINGTON
Keyword(s):  
High Frequency ◽  
Very High Frequency ◽  
Disturbing Factor ◽  
Forward Scatter ◽  
Very High

Modelling the Influence of Time and Temperature on Respiration Rate of Fresh Fig and Diced Papaya

2014 ◽  
Vol 10 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Roji B. Waghmare ◽  
Pramod V. Mahajan ◽  
Uday S. Annapure
Keyword(s):  
Weibull Distribution ◽  
Respiration Rate ◽  
Storage Temperature ◽  
Modified Atmosphere Packaging ◽  
Aerobic Condition ◽  
Distribution Model ◽  
Distribution Models ◽  
System Method ◽  
Storage Temperatures ◽  
Good Agreement

Abstract For the design of modified atmosphere packaging, it is necessary to know the influence of time and temperature on the respiration rate (RR) of fresh produce. RR of fresh fig and diced papaya was measured at three temperatures (10, 20 and 30°C) for storage time of 1–5 days under aerobic condition using closed system method. The aim was to determine the influences of storage temperature and time on RR of fresh fig and diced papaya. It develops and validates a combined predictive mathematical model based on the Arrhenius equation and Weibull distribution model. Temperature and time had a significant effect on RR. of fresh fig and diced papaya ranged from 16.2 to 45 and 25.5 to 114.9 ml kg–1 h–1 and ranged from 11.5 to 51.9 and 23.9 to 113 ml kg–1 h–1, respectively, over the three storage temperatures tested. RR increased significantly four- to fivefolds with increase in temperature from 10 to 30°C. Temperature and the interaction of time and temperature had the significant effect on and . Arrhenius and Weibull distribution models successfully fitted the experimental data, adequately describing the influence of temperature and time on RR of fresh fig and diced papaya. This model can be used to predict RR at different temperature and time. The model which was tested at 15°C for its validity showed good agreement between experimental and predicted data. These models would help to choose the optimum packaging for selected fruits.

THE ANALYSIS OF TWO-PROFILE ENVIRONMENT GROUND CLUTTER STATISTICS MEASURED USING FORWARD SCATTER RADAR WITH VHF AND UHF BANDS

2016 ◽  
Vol 78 (7) ◽  
Author(s):  
N. N. Ismail ◽  
N. E. A. Rashid ◽  
Z. Ismail Khan ◽  
N. Ripin ◽  
M. F. Abdul Rashid
Keyword(s):  
Carrier Frequency ◽  
High Frequency ◽  
Goodness Of Fit ◽  
Weibull Model ◽  
Measured Signal ◽  
Very High Frequency ◽  
Chi Square ◽  
Distribution Method ◽  
Ground Clutter ◽  
Log Normal

In this paper, a FSR two-profile environment ground clutter-measured signal with very high frequency (VHF) and ultra high frequency (UFH) at a border of dense forest and free space area are presented. Statistical distribution method is used to model the clutter signal, namely Weibull, Gamma, Log-Logistic and Log-Normal distribution. Two goodness-of-fit (GOF) tests are used to calculate the error between the amplitude of the clutter data and the statistical model, which are the root mean square error (RMSE) and chi-square (CS). At the end of this analysis, Weibull model was found to be the best fit for 64 MHz clutter signal while Gamma model is best fitted at 151 MHz carrier frequency. Another model known as Log-Logistic model fits well to a clutter signal measured with 434 MHz carrier frequency.

scholarly journals Comparative Evaluation of Probability Distribution Models of Flood flow in Lower Niger Basin

2021 ◽  
Vol 6 (2) ◽  
pp. 107-117
Author(s):  
Itolima Ologhadien
Keyword(s):  
Water Resources ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Distribution Model ◽  
Distribution Models ◽  
Optimum Distribution ◽  
Water Resources Systems ◽  
Type Iii ◽  
Pearson Type

The choice of optimum probability distribution model that would accurately simulate flood discharges at a particular location or region has remained a challenging problem to water resources engineers. In practice, several probability distributions are evaluated, and the optimum distribution is then used to establish the quantile - probability relationship for planning, design and management of water resources systems, risk assessment in flood plains and flood insurance. This paper presents the evaluation of five probability distributions models: Gumbel (EV1), 2-parameter lognormal (LN2), log pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value (GEV) using the method of moments (MoM) for parameter estimation and annual maximum series of five hydrological stations in the lower Niger River Basin in Nigeria. The choice of optimum probability distribution model was made on five statistical goodness – of – fit measures; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR), and probability plot correlation coefficient (PPCC). The results show that GEV is the optimum distribution in 3 stations, and LP3 in 2 stations. On the overall GEV is the best – fit distribution, seconded by PR3 and thirdly, LP3. Furthermore, GEV simulated discharges were in closest agreement with the observed flood discharges. It is recommended that GEV, PR3 and LP3 should be considered in the final selection of optimum probability distribution model in Nigeria.

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