scholarly journals Best fit probability distribution analysis of precipitation and potential evapotranspiration of India’s highly dense population state - Bihar

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 139-150
Author(s):  
VIKRAM KUMAR ◽  
SHAKTI BALA ◽  
BHAR TESH
Keyword(s):  
Probability Distribution ◽  
Policy Development ◽  
Goodness Of Fit ◽  
Potential Evapotranspiration ◽  
Temporal Distribution ◽  
Cauchy Distribution ◽  
The State ◽  
Anthropogenic Factors ◽  
Distribution Analysis ◽  
Best Fit

Planning of water resources and its management with the ambiguity and non-uniformity accompanying with precipitation and other meteorological physical characteristics may perhaps effect on agricultural production in Bihar where the farmers mostly depend on precipitation. The precipitation and potential evapotranspiration temporal distribution of the state is irregular due to geomorphology, climatic and other anthropogenic factors of the state. In the present study, attempt is taken to expose the best-fit probability distribution among the various available probability distribution of annual average precipitation and potential evapotranspiration based on 102 year of past records of all 37 districts of the state. On the basis of ranks of goodness of fit tests such as Kolmogorov Smirnov, Anderson Darling and Chi-Squared, the normal distribution was observed the best-fit probability distribution for 11 districts followed by Weibull (3P) for 9 districts, the Beta distribution for 5 districts and other distribution for rest districts for precipitation. Whereas Cauchy distribution was come out with the best-fit probability distribution for potential evapotranspiration for all districts and the second best was Gamma (3P) covering almost 60% of the total districts followed by General Extreme Value distribution (32%). The results can be used in future hydraulic design, hydrological study for estimation of return period and water resource planners for policy development.  

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

scholarly journals A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan

2016 ◽  
Vol 11 (1) ◽  
pp. 432-440 ◽  
Author(s):  
M. T. Amin ◽  
M. Rizwan ◽  
A. A. Alazba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Future Research ◽  
Maximum Rainfall ◽  
Type Iii ◽  
Goodness Of Fit Tests ◽  
Pearson Type ◽  
Northern Regions ◽  
Best Fit

AbstractThis study was designed to find the best-fit probability distribution of annual maximum rainfall based on a twenty-four-hour sample in the northern regions of Pakistan using four probability distributions: normal, log-normal, log-Pearson type-III and Gumbel max. Based on the scores of goodness of fit tests, the normal distribution was found to be the best-fit probability distribution at the Mardan rainfall gauging station. The log-Pearson type-III distribution was found to be the best-fit probability distribution at the rest of the rainfall gauging stations. The maximum values of expected rainfall were calculated using the best-fit probability distributions and can be used by design engineers in future research.

Temporal and spatial variations in annual rainfall distribution in Erbil province

2018 ◽  
Vol 47 (1) ◽  
pp. 59-67
Author(s):  
Tariq H Karim ◽  
Dawod R Keya ◽  
Zahir A Amin
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Kendall Test ◽  
Distribution Functions ◽  
Annual Rainfall ◽  
Maximum Rainfall ◽  
Probability Distribution Functions ◽  
Goodness Of Fit Tests ◽  
Annual Maximum Rainfall ◽  
Best Fit

This study aimed to determine the best fit probability distribution of annual maximum rainfall using data from nine stations within Erbil province using different statistical analyses. Nine commonly used probability distribution functions, namely Normal, Lognormal (LN), one-parameter gamma (1P-G), 2P-G, 3P-G, Log Pearson, Weibull, Pareto, and Beta, were assessed. On the basis of maximum overall score, obtained by adding individual point scores from three selected goodness-of-fit tests, the best fit probability distribution was identified. Results showed that the 2P-G distribution and LN distribution were the best fit probability distribution functions for annual rainfall for the region. The analysis of annual rainfall records in Erbil city spanning from 1964 to 2013, covering three periods, also revealed significant temporal changes in the shape and scale parameter patterns of the fitted gamma distribution. Based on the reliable annual rainfall data in the region, the shape and scale parameters were then regionalized, hence it is possible to find the parameter values for any desired location within the study area. The Mann–Kendall test results indicated that there was a decreasing trend in rainfall over most of the study area in recent decades.

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.

scholarly journals Distribution of Earthquake Magnitude Levels in Bangladesh

2019 ◽  
Vol 11 (3) ◽  
pp. 15
Author(s):  
Md. Habibur Rahman ◽  
Md. Moyazzem Hossain
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Earthquake Magnitude ◽  
Human Beings ◽  
Richter Scale ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Best Fit

Earthquakes are one of the main natural hazards which seriously make threats the life and property of human beings. Different probability distributions of the earthquake magnitude levels in Bangladesh are fitted. In terms of graphical assessment and goodness-of-fit criterion, the log-normal distribution is found to be the best fit probability distributions for the earthquake magnitude levels in Bangladesh among the probability distribution considered in this study. The average earthquake magnitude level found 4.67 (in Richter scale) for log-normal distribution and the approximately forty-six percent chance is predicted to take place earthquake magnitude in the interval four to five.

scholarly journals ​Rainfall Probability Distribution and Forecasting Monthly Rainfall of Navsari using ARIMA Model

2021 ◽  
Author(s):  
D.K. Dwivedi ◽  
P.K. Shrivastava
Keyword(s):  
Probability Distribution ◽  
Stochastic Processes ◽  
Method Of Moments ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Goodness Of Fit Test ◽  
Sowing Time ◽  
Best Fit

Background: Reliable rainfall forecast could be helpful to farmers as major decisions regarding selection of crops and sowing time are based on the rainfall. The univariate time series ARIMA model requires only past data for model formulation and to simulate stochastic processes. The current study was aimed to obtain the probability distribution of monthly rainfall using the method of moments and to forecast rainfall using the ARIMA model. Methods: The method of moments was used to determine the parameters of distributions and the chi-square test was used as a goodness of fit test to obtain the best fit distribution for monthly rainfall of Navsari, Gujarat utilizing 36 years of rainfall data. Auto regressive moving average (ARIMA) model, popular owing to its simplicity and ability to simulate various stochastic processes was used in the study. Result: It was revealed that the Weibull distribution was the best fit distribution for June and September, whereas Gumbel was the best fit distribution for July. For simulating monthly rainfall, the seasonal ARIMA model (0,0,1) (0,1,1)12 was found to be the appropriate model based on its performance. The model had the least root mean square value and also the residuals were found to have no correlation.

scholarly journals Frequency analysis of annual one day to five consecutive days maximum rainfall for Gandak river basin

2014 ◽  
Vol 3 (2) ◽  
pp. 93 ◽  
Author(s):  
Shivika Singla ◽  
Raktim Halder ◽  
Rakesh Khosa ◽  
Rumani Singla ◽  
Rudraksh Rajeev
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Frequency Estimation ◽  
Distribution Functions ◽  
Chi Square ◽  
Maximum Rainfall ◽  
Thiessen Polygon ◽  
Chi Square Test ◽  
Arc Gis ◽  
Best Fit

The present study has been conducted for rainfall intensity and frequency estimation for the Gandak basin, a region prone to high floods with an unrealized and unexplored hydro-potential. The two popular gridded precipitation datasets i.e.: (1) APHRODITE, and (2) IMD, for the years 1969-2005, has been used to calculate the mean basin precipitation through the Thiessen polygon method on the ARC-GIS interface. This computed data was used to find out the 1-day, 2-day to 5-day consecutive maximum precipitation series and hence fitted into various well-known probability distribution functions viz., Normal, Gamma, Exponential, etc. According to the best fit data in these functions, the quantiles were determined corresponding to a return period of 2, 10, 20, 25, 50 and 100 years. The two widely used tests: Chi-square Test and Kolmogorov-Smirnov Test were employed to further check the goodness of fit of the series in the distributions. The results reveal that the best fit for 1-day was achieved with the normal distribution, for 2-day with GEV and with GPAR for the remaining maximum consecutive days rainfall. Such studies have thus proven to be substantially facilitative in planning for the safe and economic design of various engineered structures such as bridges, culverts, levees, canals, irrigation and drainage works and effective reservoir management. Keywords: Floods, Frequency, Hydrology, Probability Distribution, Rainfall.

scholarly journals Evaluation of Best-Fit Probability Distribution Models for the Prediction of Inflows of Kainji Reservoir, Niger State, Nigeria

2017 ◽  
Vol 10 ◽  
pp. 117862211769103
Author(s):  
Mohammed J Mamman ◽  
Otache Y Martins ◽  
Jibril Ibrahim ◽  
Mohammed I Shaba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Type I ◽  
Distribution Models ◽  
Goodness Of Fit Test ◽  
Hydropower Dams ◽  
Log Normal ◽  
Near Future ◽  
Best Fit ◽  
Analysis Of Time Series

The analysis of time series is essential for building mathematical models to generate synthetic hydrologic records, to forecast hydrologic events, to detect intrinsic stochastic characteristics of hydrologic variables, as well as to fill missing and extend records. To this end, various probability distribution models were fitted to river inflows of Kainji Reservoir in New Bussa, Niger State, Nigeria. This is to evaluate the probability function that is best suitable for the prediction of their values and subsequently using the best model to predict for both the expected maximum and minimum monthly inflows at some specific return periods. Three models, ie, Gumbel extreme value type I (EVI), log-normal (LN), and normal (N), were evaluated for the inflows and the best model was selected based on the statistical goodness-of-fit test. The values of goodness-of-fit test for Kainji hydropower dam are as follows: r = 0.96, R2 = 0.99, SEE = 0.0087, χ2 = 0.0054, for Gumbel (EVI); r = 0.79, R2 = 0.85, SEE = 0.02, χ2 = 0.31 for LN; and r = 0.0.75, R2 = 0.0.68, SEE = 0.056, χ2 = 1376.39 for N. For the Kainji hydropower dams, the Gumbel (EVI) model gave the best fit. These probability distribution models can be used to predict the near-future reservoir inflow at the Kainji hydropower dams.

STUDYING OF TERSKO-SULAKSKAYA LOWLAND VEGETATION UNDER THE RESULTS OF GROUND-BASED RESEARCH AND AUTOMATED INTERPRETATION

1970 ◽  
pp. 29-32
Author(s):  
M. I. Dzhalalova ◽  
A. B. Biarslanov ◽  
D. B. Asgerova
Keyword(s):  
Plant Communities ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Impacts ◽  
Species Abundance ◽  
Remote Sensing Data ◽  
Indigenous Communities ◽  
The State ◽  
Anthropogenic Factors ◽  
Internet Resource

The state of plant communities in areas located in the Tersko-Sulak lowland was studied by assessing phytocenotic indicators: the structure of vegetation cover, projective cover, species diversity, species abundance and elevated production, as well as automated decoding methods. There are almost no virgin soils and natural phytocenoses here; all of them have been transformed into agrocenoses (irrigated arable lands and hayfields, rice-trees and pastures). The long-term impact on pasture ecosystems of natural and anthropogenic factors leads to significant changes in the indigenous communities of this region. Phytocenoses are formed mainly by dry-steppe types of cereals with the participation of feather grass, forbs and ephemera, a semi-desert haloxerophytic shrub - Taurida wormwood. At the base of the grass stand is common coastal wormwood and Taurida wormwood - species resistant to anthropogenic influences. Anthropogenic impacts have led to a decrease in the number of species of feed-rich grain crops and a decrease in the overall productivity of pastures. Plant communities in all areas are littered with ruderal species. The seasonal dynamics of the land cover of the sites was estimated by the methods of automatic decoding of satellite images of the Landsat8 OLI series satellite for 2015, dated by the periods: spring - May 20, summer - July 23, autumn - October 20. Satellite imagery data obtained by Landsat satellite with a resolution in the multispectral image of 30 m per pixel, and in the panchromatic image - 10 m per pixel, which correspond to the requirements for satellite imagery to assess the dynamics of soil and vegetation cover. Lower resolution data, for example, NDVI MODIS, does not provide a reliable reflection of the state of soil and vegetation cover under arid conditions. In this regard, remote sensing data obtained from the Internet resource https://earthexplorer.usgs.gov/ was used.

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