Generalized Logistic Research Articles

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Estimation of regional and at-site quantiles of extreme winds under flood index procedure

Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessary not only for a comprehensive understanding of wind hazards but also for infrastructure design and safety, wind energy potential, disaster risk reduction, insurance and risk assessment in a particular region of study. This study investigated regional frequency analysis of DAMWS of Baluchistan and Sindh provinces of Pakistan. L-moments regionalization techniques along with flood index procedure were applied to DAMWS records of 21 stations from 1990 to 2019 across the study area. We intended to find the regional frequency distribution for maximum winds and predict the returns for extreme winds events in the future. Only one station namely Lasbella was found to be discordant. With the help of cluster analysis, the remaining 20 stations were further divided into two homogeneous. Heterogeneity measures validate that both regions are homogenous with allotted stations. Regional quantiles for both regions are estimated through best-fit probability distribution among Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type 3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV). Robustness of GLO distribution compared to GEV distribution is assessed through Monte Carlo simulations of relative bias and relative root mean square error. Findings clearly show that GLO distribution is the best for regional modeling. Furthermore, with the help of index flood procedure we determined at-site quantiles of all stations for various return periods. These estimated quantiles are of valuable information for various sectors, including infrastructure, energy, disaster management, and climate resilience, leading to improved planning, development, and risk reduction in the face of wind-related hazards in Sindh and Balochistan provinces of Pakistan.

Determining optimal probability distributions for gridded precipitation data based on L-moments

Understanding the probability distributions of precipitation is crucial for predicting climatic events and constructing hydraulic facilities. To overcome the inadequacy of precipitation data, regional frequency analysis was commonly used by “trading space for time”. However, with the increasing availability of gridded precipitation datasets with high spatial and temporal resolutions, the probability distributions of precipitation for these datasets have been less explored. We used L-moments and goodness-of-fit criteria to identify the probability distributions of annual, seasonal, and monthly precipitation for a 0.5° × 0.5° dataset across the Loess Plateau (LP). We examined five 3-parameter distributions, namely General Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), and Pearson type III (PE3), and evaluated the accuracy of estimated rainfall using the leave-one-out method. We also presented pixel-wise fit-parameters and quantiles of precipitation as supplements. Our findings indicated that precipitation probability distributions vary by location and time scale, and the fitted probability distribution functions are reliable for estimating precipitation under various return periods. Specifically, for annual precipitation, GLO was prevalent in humid and semi-humid areas, GEV in semi-arid and arid areas, and PE3 in cold-arid areas. For seasonal precipitation, spring precipitation mainly conforms to GLO distribution, summer precipitation around the 400 mm isohyet prevalently follows GEV distribution, autumn precipitation primarily meets GPA and PE3 distributions, and winter precipitation in the northwest, south, and east of the LP mainly conforms to GPA, PE3 and GEV distributions, respectively. Regarding monthly precipitation, the common distribution functions are PE3 and GPA for the less-precipitation months, whereas the distribution functions of precipitation for more-precipitation months vary substantially across different regions of the LP. Our study contributes to a better understanding of precipitation probability distributions in the LP and provides insights for future studies on gridded precipitation datasets using robust statistical methods.

Accuracy Evaluation of Standardized Precipitation Index (SPI) Estimation under Conventional Assumption in Yeşilırmak, Kızılırmak, and Konya Closed Basins, Turkey

The doubt in the calculation algorithm of the standardized precipitation index (SPI), which is widely preferred in the evaluation and monitoring of drought, still remains up-to-date because its calculation process is performed in the form of standardization or normalization with a default probability distribution. Therefore, the success of this index is directly affected by the choice of the probability distribution model. This study is based on the effect of three different parameter estimation methods on the calculation process, as well as the comparison of the SPI results calculated based on the default Gamma distribution and the distribution with the best ability to represent the 3-and 12-month consecutive summed rainfall data among the 15 candidate distributions namely Gamma (GAM), Generalized Extreme Value (GEV), Pearson Type III (P III), Log Pearson Type III (LP III), two-parameter Lognormal (LN2), three-parameter Lognormal (LN3), Generalized Logistic (GLOG), Extreme Value Type I (EVI), Generalized Pareto (GPAR), Weilbul (W), Normal (N), Exponential (EXP), Logistic (LOG), four-parameter Wakeby (WK4), and five-parameter Wakeby (WK5) distributions. Approximately 68.4% and 18.4% of the 3-month data considered had the best fit to the Weibull and Pearson III distribution, while approximately 24% and 18% of the 12-month data had the best fit to the Weibull and Logistic distribution. On the other hand, it was found that the default Gamma distribution calculated the extreme drought categories significantly more than the best-fit distribution model. In terms of parameter estimation methods, L-moments for 3-month series and maximum likelihood approaches for 12-month series were most dominant.

Standardized precipitation evapotranspiration index (SPEI) for Canada: assessment of probability distributions

Candidate probability distributions for the Standardized Precipitation Evapotranspiration Index (SPEI) for Canada were examined using the Canadian Gridded (CANGRD) temperature and precipitation dataset and CMIP5 projections. The probability distribution is a core component to the calculation of standardized values. For SPEI, a continuous probability distribution is fitted to the water balance time series so that the resulting transformed index follows a standard normal distribution. Selection of an appropriate distribution is therefore important as an inappropriate distribution may lead to biased values and subsequently affect the interpretation of SPEI results. Candidate distributions considered for SPEI included the generalized logistic (GLO), generalized extreme value (GEV), Pearson Type III (PE3) and normal (NOR) distributions. A range of goodness of fit tests were used to assess how well the distributions fit SPEI. Differences in CANGRD SPEI time series between three pairs of distributions, GLO-GEV, GLO-PE3, and GEV-PE3, showed that there were larger differences in SPEI values between GLO and the other two distributions than the difference between GEV and PE3. SPEI results fitted with GLO resulted in lower threshold exceedances of extreme SPEI (-/+2) than GEV and GLO. A comparison between CMIP5 SPEI values fitted with GLO (SPEI-GLO) and PE3 (SPEI-PE3) showed that there were seasonal and spatial variations in results, although the use of multi-model ensembles reduced these differences. As was the case for CMIP5 SPEI-GLO projections, projected annual changes in CMIP5 SPEI-PE3 show an increase in drying at the surface in central/southern Canada. Overall, the study recommends using PE3 or GEV for SPEI analysis for Canada.

A study on modeling growth model of Adana pigeons

The study aims to determine a mathematical model that can be used to describe the growth of the Adana pigeon. Since pigeons have only one breeding season, just one or two pairs of baby pigeons are raised per year. Hatchlings sometimes die before reaching adulthood. For this reason, measurements can be taken for 10, 15 and 60 days periods. Related with this issue, only 43-days measurements of 68 pigeons are used over a 6-year period. The study is modelled by taking the day-to-day average of the data (43 days) of 68 pigeons. The study was conducted on 68 Adana pigeons in the interval between the age of 1 and 43 days. The growth of pigeon cub was measured by daily live weight until 1 to 43 days. The estimation is carried out by writing the specific Matlab codes. Classical growth functions used in animals are in nonlinear form. Various numerical methods have been developed to estimate parameters in nonlinear functions. Special program routines have been developed to implement these methods. In these nonlinear models, there are more than one parameter to be estimated. Therefore, the number of mathematical operations in estimating the parameters is large. The most used models in the literature are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. However, as far as is known, there is no published article for Adana pigeons that uses all of these models and compares which one is better. These models are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. The best analysis was done by the Richards model in terms of both the Mean Squared Error (MSE), mean absolute percentage error (MAPE) and (Coefficient of Determination) R2 .

FORECASTING THE COVID-19 USING THE DISCRETE GENERALIZED LOGISTIC MODEL

Using mathematical models to describe the dynamics of infectious-diseases transmission in large communities can help epidemiological scientists to understand different factors affecting epidemics as well as health authorities to decide measures effective for infection prevention. In this study, we use a discrete version of the Generalized Logistic Model (GLM) to describe the spread of the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia. We assume that we are operating in discrete time so that the model is represented by a first-order difference equation, unlike time-continuous models, which employ differential equations. Using this model, we forecast COVID-19 spread in Saudi Arabia and we show that the short-term predicted number of cumulative cases is in agreement with the confirmed reports.

In Situ Flood Frequency Analysis Used for Water Resource Management in Kelantan River Basin

One of the most prevalent and traditional uses of statistics in hydrology is flood frequency analysis. The flood can occur practically everywhere and is considered the leading cause of natural disaster death worldwide. This study aims to apply the flood frequency analysis of the Kelantan streamflow site to identify the optimal distribution that best fits the flood frequency data from the goodness-of-fit test (GOF). Five distributions were applied in this study; namely lognormal (LG), generalized extreme value (GEV), generalized Pareto (GP), log-Pearson three (L3) and generalized logistic (GL) distribution. to obtain the parameter estimates. The distribution performance evaluation is then performed utilizing the GOF and efficiency evaluations. The results indicate that the generalized GP distribution is the best possible function for determining the annual peak flow at the Kelantan streamflow site.

Regional Analysis of Consecutive Days Maximum Rainfall Using L-Moments: A Case Study for Brahmaputra Valley Region of Assam, India

In this study, Regional Rainfall Frequency analysis has been carried out based on L-moment in the Brahmaputra Valley. For this purpose, the consecutive 1 to 6 days maximum rainfall for 11 sites (data varying from 14 years to 23 years) have been considered for our study. The heterogeneity measures for 1 to 6 days of the study region are calculated. The five probability distributions namely, generalized extreme value (GEV), generalized logistic (GLO), generalized Pareto (GPA), generalized normal (GNO), and the Pearson Type III (PE3) have been considered. The Z-statistics criteria and L-moment ratio diagram for goodness of fit tests have been used for identifying the best fitting distributions for 1 to 6 days of the study area. The PE3 distribution is identified as the best fitting distribution for the consecutive 1-day and 6-days maximum rainfall while GLO distribution for the consecutive 2-days and 4-days, GNO distributions for the consecutive 3-days and 5-days maximum rainfall of the study region. The regional relationships for each season of the study regions are develope

State-of-the-Art Statistical Approaches for Estimating Flood Events.

Reliable quantile estimates of annual peak flow discharges (APFDs) are needed for the design and operation of major hydraulic infrastructures and for more general flood risk management and planning. In the present study, linear higher order-moments (LH-moments) and nonparametric kernel functions were applied to APFDs at 18 stream gauge stations in Punjab, Pakistan. The main purpose of this study was to evaluate the impacts of different quantile estimation methods towards water resources management and engineering applications by means of comparing the state-of-the-art approaches and their quantile estimates calculated from LH-moments and nonparametric kernel functions. The LH-moments (η = 0, 1, 2) were calculated for the three best-fitted distributions, namely, generalized logistic (GLO), generalized extreme value (GEV), and generalized Pareto (GPA), and the performances of these distributions for each level of LH-moments (η = 0, 1, 2) were compared in terms of Anderson–Darling, Kolmogorov–Smirnov, and Cramér–Von Mises tests and LH-moment ratio diagrams. The findings indicated that GPA and GEV distributions were best fitted for most stations, followed by GLO distribution. The quantile estimates derived from LH-moments (η = 0, 1, 2) had a lower relative absolute error, particularly for higher return periods. However, the Gaussian kernel function provided a close estimate among nonparametric kernel functions for small return periods when compared to LH-moments (η = 0, 1, 2), thus highlighting the importance of using LH-moments (η = 0, 1, 2) and nonparametric kernel functions in water resources management and engineering projects.

Evaluation of probabilistic models for characterizing design low-flows of River Ogun, Southwest Nigeria

Information of low-flow is important for maintaining instream flow, conserving biodiversity, enhancing food production, industrial abstraction, tourism and dilution of effluents from industries and households. This study establishes suitable probabilistic models for characterizing different durations of design low-flows of Ogun River. The adequacy of fit of four probability distributions, namely Reversed Generalized Extreme Value (GEVR) distribution, Generalized Normal (GNO) distribution, Generalized Logistic (GLO) distribution and Pearson Type III (PE3) were evaluated using the Anderson-Darling (A2) goodness-of-fit statistic and the D-index diagnostic test. The study revealed that GLO is best suited for predicting the annual minimal, 3-day minima, 7-day minima, 10-day minima, 15-day minima and 30-day minima based on the A2 and D-index values. Six mathematical models derived from probability plots were established to relate the different low-flow series to their non-exceedance probability. The models could be used for characterizing low-flows and for water resources management of Ogun River Basin.

Desenvolvimento de Curvas de Intensidade-Duração-Frequência de Chuvas Intensas com ênfase no Comportamento da Cauda Superior da Distribuição

Abstract The design and management of various hydraulic structures (such as stormwater drains, bridges and dams) require the estimation of rainfall with duration of a few minutes up to 24 h or more. Intensity-duration-frequency (IDF) curves links probability of occurrence to a given rainfall intensity. The procedure for obtaining IDF curves basically involves two steps: (i) frequency analysis for different durations and (ii) modeling of IDF curves. In the first step, this study aimed to adequately select the upper tail weight of the following distributions: generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA). In the second step, this study aimed to evaluate the performance of three models of IDF curves. The traditional model (M1) was compared with empirical model (M2) and a second-order polynomial model (M3). To perform this study, rainfall data from the city of Caraguatatuba (São Paulo state, Brazil) for the period between 1971 and 2001 were used, for time intervals between 10 and 1440 min. The main conclusions were: (i) GLO and GEV had heavy upper tail while GPA had light upper tail, impacting quantiles with T > 100 years; (ii) M3 presents errors lower than M1 for return periods greater than 100 years.

Regional frequency analysis of daily maximum rainfall in Haryana

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( DIST Z ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions;Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

Regional frequency analysis of daily maximum rainfall in Haryana

This paper is sets-out for the regional frequency analysis of daily maximum rainfall from the 27 rain gauge stations in Haryana using L-moments. As the distribution of rainfall varies spatially in Haryana, the 27 rain gauge stations are grouped into three clusters namely, cluster C1, C2 and C3 using Ward’s clustering method and homogeneity of clusters was confirmed using L-moments-based Heterogeneity measure (H). Using goodness-of-fit measure ( ) and L-moment ratios diagram, suitable regional frequency distributions were selected among five candidate distributions; Generalized Logistic (GLO), Generalized Extreme Value (GEV),Generalized Normal (GNO), Generalized Pareto (GPA), and Pearson Type-3 (PE3) for each cluster. Results showed that PE3 and GNO were good fitted regional distribution for the cluster C1 and GEV, PE3 and GNO fitted for cluster C2 while for cluster C3; GLO and GEV were good fitted regional distribution. To select a robust distribution among good fitted distributions accuracy measures calculated using Monte Carlo simulations for each cluster. The simulation result showed that PE3 was the best choice for quantile estimation for cluster C1. For cluster C2, PE3 was the best choicefor a large return period and GEV was best for a small return period. For cluster C3, GEV was the most suitable distribution for quantile estimation. Using these robust distributions rainfall quantiles were estimated at each rain gauge station from 2 to 100 year return periods. These estimated rainfall quantiles may be rough guideline for planning and designing hydraulic structures by policy makers and structural engineers.

Flood Frequency Analysis using L-Moment For Segamat River

Flood frequency analysis is critical in water system design and estimating flood recurrence. This study aims to conduct the flood frequency analysis on Segamat River streamflow site to find the optimum distribution that fits the flood frequency data. In terms of estimating parameters, the L-moment method is more robust and more efficient than the maximum likelihood method. Besides, the L-moment method is not affected by sampling variability. Therefore, in this study, we applied the technique of L-moment for parameter estimation on five candidate distributions, the generalised Pareto (GPA) distribution, generalised extreme value (GEV) distribution, generalised logistic (GLO) distribution, log-Pearson 3 (LP3) distribution, and log-normal (LN3) distribution. The rank score approach is implemented to determine the optimum distribution for the annual Segamat River peak flow. Probability distribution identification is essential and it is a fundamental step in statistical analysis. The goodness of fit test and efficiency assessment are employed to evaluate the distributions' performance. The results show that the LN3 distribution is selected as the optimum function for the yearly peak flow for the Segamat River streamflow site. The outcome of this study can be used to understand the flood frequency analysis for the Segamat River.

Power Bayesian Markov Chain Monte Carlo (MCMC) for Modelling Extreme Temperatures in Sumatra Island Using Generalised Extreme Value (GEV) and Generalised Logistic (GLO) Distributions

Power Bayesian Markov Chain Monte Carlo (MCMC) for Modelling Extreme Temperatures in Sumatra Island Using Generalised Extreme Value (GEV) and Generalised Logistic (GLO) Distributions

Regional Precipitation-Frequency Analysis in Serbia Based on Methods of L-Moment

Precipitation is the main water resource for agronomy. The frequency is one of the most significant characteristics of precipitation. The objectives of this study are (1) to identify the homogenous regions, (2) to determine the probability distribution that fits precipitation the best for each region, (3) to perform regional precipitation-frequency analysis by using L-moment methods, and (4) to derive the return levels for precipitation. In this study, the precipitation data collected from 24 meteorological stations for the period 1965–2013 over Serbia were analyzed. Three distributions [generalized extreme value (GEV), generalized Pareto (GPD), and generalized logistic (GLO)] based on three parameters (scale, shape, and location) are investigated. The L-moment method is applied to determine the parameters. Three independent precipitation regions (R1, R2, and R3) were studied. The homogeneity test indicates that the identified regions are homogenous. To confirm the goodness-of-fit for the selected three probability distributions, the Z-statistics was applied. Based on the obtained results, the GEV distribution best fits the precipitation data in the regions R1 and R2, while the GPD was selected for the region R3.

Quantifying Uncertainty in the Modelling Process; Future Extreme Flood Event Projections Across the UK

With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) events—may change at a regional level across the UK by the 2080s (2069–2098). Capturing uncertainty in the hydroclimatic modelling chain, flow projections were extracted from the EDgE (End-to-end Demonstrator for improved decision-making in the water sector in Europe) multi-model ensemble: five Coupled Model Intercomparison Project (CMIP5) General Circulation Models and four hydrological models forced under emissions scenarios Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 (5 × 4 × 2 chains). Uncertainty in extreme value parameterisation was captured through consideration of two methods: generalised extreme value (GEV) and generalised logistic (GL). The method was applied across 192 catchments and aggregated to eight regions. The results suggest that, by the 2080s, many regions could experience large increases in extreme runoff, with a maximum mean change signal of +34% exhibited in East Scotland (1:2-year RP). Combined with increasing urbanisation, these estimates paint a concerning picture for the future UK flood landscape. Model chain uncertainty was found to increase by the 2080s, though extreme value (EV) parameter uncertainty becomes dominant at the 1:30-year RP (exceeding 60% in some regions), highlighting the importance of capturing both the associated EV parameter and ensemble uncertainty.

Türkiye'de 17 Mart ile 12 Temmuz 2020 Arasında COVID-19 Vakaları Kullanılarak Büyüme Modellerinin Belirlenmesi Üzerine Bir Çalışma: Kesitsel Çalışma

Objective: We use the most frequently used growth functions in the literature for estimating Turkey's cumulative number of confirmed coronavirus disease-2019 (COVID-19) cases. We analyze Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka models for determining the appropriate model. Material and Methods: The number of cases of COVID-19 in Turkey between March 17, 2020 and July 12, 2020 were included in the study. The data used in the study were obtained from Johns Hopkins University. We used the most frequently used Non-linear growth models in the literature for estimating Turkey's cumulative number of confirmed COVID-19 cases. Matlab software was used to estimate the parameters in the models studied. non-linear least squares regression function is used in Matlab software. Results: According to the estimation results, the best fitting model is the Richards model in terms of both the mean squared error and R2 (coefficient of determination). Conclusion: We recommend to use the Richards model in modeling the cumulative COVID-19 cases. For estimating the future cases, it will be appropriate to use the Richards model. In addition, estimates of the number of daily cases obtained from the Richards model are not compatible with the actual number of daily cases. This may occur because estimations on these growth models can sometimes lead to erroneous results.

On the wind resource in Algeria: Probability distributions evaluation

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.