Rainfall Intensity-Duration-Frequency Models for Ibadan Using Optimization Technique

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
A.O David
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Optimization Technique ◽  
Extreme Value ◽  
Engineering Structures ◽  
Distribution Ranges ◽  
Pearson Type ◽  
Intensity Duration Frequency ◽  
Type 3

Hydraulic engineering structures always depend on type and velocity of flow water arising from rainfall activities in a given catchment. Twenty-five (25) year Ibadan daily rainfall data (amount & duration) was collected from Nigerian Meteorological Agency (NIMET) Abuja and subjected to frequency analysis for the development of intensity – duration – frequency models. Mean rainfall amounts with durations of 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 420 minutes were extracted and subjected to frequency analysis using the Excel Optimization Solver wizard. Gumbel Extreme Value Type 1 and Log- Pearson Type 3 distributions were used to develop specified and non-specified IDF models for return periods of 2, 5, 10, 25, 50 and 100 years. These models have not been developed for Ibadan. The values for the coefficient of determination (R2) and mean squared error (MSE) were used to test the fitness of the probability distribution functions. Gumbel Extreme Value Type – 1 has values of R2 and MSE ranging from 0.989 to 0.998 & 3.54 to 102.30 while R2 and MSE values for Log – Pearson type 3 distribution ranges from 0.978 to 0.989 & 6.09 to 213.10. The probability distribution models are recommended for the prediction of rainfall intensities of Ibadan metropolis for the Ministry of Works for adequate design purposes.

scholarly journals Modelling Rainfall Intensity by Optimization Technique in Abeokuta, South-West, Nigeria

2019 ◽  
pp. 1-10
Author(s):  
A. O. David ◽  
Ify L. Nwaogazie ◽  
J. C. Agunwamba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Optimization Technique ◽  
Distribution Models ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Pearson Type ◽  
Type 3

The design of water resources engineering control structures is best achieved with adequate estimation of rainfall intensity over a particular catchment. To develop the rainfall intensity, duration and frequency (IDF) models, 25 year daily rainfall data were collected from Nigerian Meteorological Agency (NIMET) Abuja for Abeokuta. The annual maximum rainfall amounts with durations of 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 420 minutes were extracted and subjected to frequency analysis using the Excel Optimization Solver wizard. Specific and general IDF models were developed for return periods of 2, 5, 10, 25, 50 and 100 years using the Gumbel Extreme Value Type -1 and Log Pearson Type -3 distributions. The Anderson-Darling goodness of fit test was used to ascertain the best fit probability distribution. The R2 values range from 0.973 – 0.993 and the Mean Squared Error, MSE from 84.49 – 134.56 for the Gumbel and 0.964 – 0.997 with MSE of 42.88 – 118.68 for Log Pearson Type -3 distribution, respectively. The probability distribution models are recommended for the prediction of rainfall intensities for Abeokuta metropolis.

scholarly journals Development of Models for Rainfall Intensity- duration-frequency for Akure, South-west, Nigeria

2019 ◽  
pp. 457-466
Author(s):  
A. O. David ◽  
Ify L. Nwaogazie ◽  
J. C. Agunwamba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Distribution Models ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Pearson Type ◽  
Intensity Duration Frequency ◽  
Type 3

The rainfall Intensity-Duration-Frequency (IDF) relationship is widely used for adequate estimation of rainfall intensity over a particular catchment. A 25 year daily rainfall data were collected from Nigerian Meteorological Agency (NIMET) Abuja for Akure station. Twenty five year annual maximum rainfall amounts with durations of 5, 10, 15, 20, 30, 45, 60, 90, 120, 180, 240, 300 and 420 minutes were extracted and subjected to frequency analysis using the excel solver software wizard. A total of six (6) return period specific and one (1) general IDF models were developed for return periods of 2, 5, 10, 25, 50 and 100 years using Gumbel Extreme Value Type-1 and Log Pearson Type -3 distributions. Anderson Darling goodness of fit test was used to ascertain the best fit probability distribution. The R2 values range from 0.982 to 0.985 for GEVT -1 and 0.978 to 0.989 for Log Pearson type -3 while the Mean Squared Error from 33.56 to 156.50 for GEVT -1 and 43.01 to 150.63 Log Pearson Type III distributions respectively. The probability distribution models are recommended for the prediction of rainfall intensities for Akure metropolis.

scholarly journals Frequency Analysis of Low Flows

1985 ◽  
Vol 16 (2) ◽  
pp. 105-128 ◽  
Author(s):  
G. V. Loganathan ◽  
C. Y. Kuo ◽  
T. C. McCormick
Keyword(s):  
Lower Bounds ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Flow Analysis ◽  
Extreme Value ◽  
Low Flow ◽  
Type Iii ◽  
Low Flows ◽  
Pearson Type

The transformations (i) SMEMAX (ii) Modified SMEMAX (iii) Power and Probability Distributions (iv) Weibull (α,β,γ) or Extreme value type III (v) Weibull (α,β,0) (vi) Log Pearson Type III (vii) Log Boughton are considered for the low flow analysis. Also, different parameter estimating procedures are considered. Both the Weibull and log Pearson can have positive lower bounds and thus their use in fitting low flow probabilities may not be physically justifiable. A new derivation generalizing the SMEMAX transformation is proposed. A new estimator for the log Boughton distribution is presented. It is found that the Boughton distribution with Cunnane's plotting position provides a good fit to low flows for Virginia streams.

scholarly journals Selecting the best probability distribution for at-site flood frequency analysis; a study of Torne River

2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Mahmood Ul Hassan ◽  
Omar Hayat ◽  
Zahra Noreen
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Direct Method ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Goodness Of Fit Tests ◽  
Pearson Type

AbstractAt-site flood frequency analysis is a direct method of estimation of flood frequency at a particular site. The appropriate selection of probability distribution and a parameter estimation method are important for at-site flood frequency analysis. Generalized extreme value, three-parameter log-normal, generalized logistic, Pearson type-III and Gumbel distributions have been considered to describe the annual maximum steam flow at five gauging sites of Torne River in Sweden. To estimate the parameters of distributions, maximum likelihood estimation and L-moments methods are used. The performance of these distributions is assessed based on goodness-of-fit tests and accuracy measures. At most sites, the best-fitted distributions are with LM estimation method. Finally, the most suitable distribution at each site is used to predict the maximum flood magnitude for different return periods.

Flood Frequency Analysis of the Mahi Basin by Using Log Pearson Type III Probability Distribution

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Uttam Pawar ◽  
Pramodkumar Hire
Keyword(s):  
Probability Distribution ◽  
Frequency Analysis ◽  
Return Period ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Series Data ◽  
Period Range ◽  
Type Iii ◽  
Pearson Type ◽  
Data Points

Flood frequency analysis is one of the techniques of examination of peak stream flow frequency and magnitude in the field of flood hydrology, flood geomorphology and hydraulic engineering. In the present study, Log Pearson Type III (LP-III) probability distribution has applied for flood series data of four sites on the Mahi River namely Mataji, Paderdi Badi, Wanakbori and Khanpur and of three sites on its tributaries such as Anas at Chakaliya, Som at Rangeli and Jakham at Dhariawad. The annual maximum series data for the record length of 26-51 years have been used for the present study. The time series plots of the data indicate that two largest ever recorded floods were observed in the year 1973 and 2006 on the Mahi River. The estimated discharges of 100 year return period range between 3676 m3/s and 47632 m3/s. The return period of the largest ever recorded flood on the Mahi River at Wankbori (40663 m3/s) is 127-yr. The recurrence interval of mean annual discharges (Qm) is between 2.73-yr and 3.95-yr, whereas, the return period of large floods (Qlf) range from 6.24-yr to 9.33-yr. The magnitude-frequency analysis curves represent the reliable estimates of the high floods. The fitted lines are fairly close to the most of the data points. Therefore, it can be reliably and conveniently used to read the recurrence intervals for a given magnitude and vice versa.

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