scholarly journals EVALUATION OF L-MOMENT AND PPCC METHOD TO DETERMINE THE BEST REGIONAL DISTRIBUTION OF MONTHLY RAINFALL DATA (CASE STUDY: NORTHWEST OF IRAN)

2013 ◽  
pp. 247-252
Author(s):  
Babak Amirataee ◽  
Majid Montaseri
Keyword(s):  
Goodness Of Fit ◽  
Regional Distribution ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Test Statistics ◽  
Northwest Of Iran ◽  
Moment Approach ◽  
Site Test ◽  
Resources Planning

The analysis and use of hydrological data for decision making in water resources planning and management can only be meaningful if the data possess the appropriate characteristics. Whereas, rainfall stations are relation together in the studying area, so that choosing a best regionally probability distribution is necessary. In this paper, probability plot correlation coefficient (PPCC) test statistics and L-moment ratio diagrams are used to determine the goodness of fit the regional distribution of monthly rainfall data in 11 stations that located in Northwest of Iran. Two methods provide Pearson III as a best regional distribution of monthly rainfall data in our study area. As regards, PPCC test has been known as a powerful single-site test among many goodness of fit, but L-Moment approach is easy and can compare the fit of several distributions to many samples of data using a single graphical instrument.

scholarly journals Probability Distribution of Rainfall in Medan

2019 ◽  
Vol 1 (2) ◽  
pp. 43-49 ◽  
Author(s):  
Elly Rosmaini
Keyword(s):  
Probability Distribution ◽  
Gamma Distribution ◽  
Goodness Of Fit ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Goodness Of Fit Test ◽  
Normal Distributions ◽  
Lognormal Distributions ◽  
Kolmogorov Smirnov ◽  
Anderson Darling

In this paper we chose three stations in Medan City , Indonesia to estimate Monthly Rainfall Data i.e. Tuntungan, Tanjung Selamat, and Medan Selayang Stations. We took the data from 2007 to 2016. In this case fitted with Normal, Gamma, and Lognormal Distributions. To estimate parameters, we used this method. Furthermore, Kolmogorov-Smirnov and Anderson Darling tests were used the goodness-of-fit test. The Gamma and Normal Distributions is suitable for Tuntungan and Medan Selayang Stations were stated by Kolmogorov-Smirnov's test. Anderson Darling's test stated that Gamma Distribution was suitable for all stations.

scholarly journals Statistical analysis of seasonal rainfall data in Chandigarh: A case study

2021 ◽  
Vol 889 (1) ◽  
pp. 012024
Author(s):  
Kaamun ◽  
Sahil Arora
Keyword(s):  
Statistical Analysis ◽  
Goodness Of Fit ◽  
Seasonal Forecast ◽  
Rainfall Data ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Maximum Rainfall ◽  
The Past

Abstract The following research focuses on Chandigarh’s annual rainfall of past 50 years i.e. from 1968 to 2017. Parameters like Kurtosis, Variance, Goodness of Fit, Mann-Kendall’s Test were performed along with total annual forecast as well as seasonal forecast was predicted. Seasonal rend was also studied so as to study in detail about the past, present, and future of rainfall in Chandigarh. This study was performed with the help of MS-Excel and ExcelStat. A rising trend was found in Chandigarh for total as well as seasonal rainfall with a maximum rainfall of 1510.9 mm in the year of 1996 and a minimum of 371.1 mm in year 1987, other than this Sen.’s slope was 6.431 whereas skewness was found to be 0.6018.

scholarly journals Analyzing the Best Fitted Probabilistic Model for the Seasonal Rainfall Data in Khulna Region of Bangladesh

2021 ◽  
Vol 2 ◽  
pp. 5
Author(s):  
K. Fatema ◽  
Muhammad Habibulla Alamin ◽  
M. Zahid Hasan ◽  
M. Murad Hossain
Keyword(s):  
Goodness Of Fit ◽  
Winter Season ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Likelihood Method ◽  
Distribution Model ◽  
Similar Amount ◽  
Post Monsoon ◽  
Goodness Of Fit Tests ◽  
Best Fit

There are several pieces of research on the statistical modeling of rainfall data in Bangladesh. Since all the seasons of a year do not receive a similar amount of rainfall, hence one single statistical model might not be able to explain the pattern of rainfall at any season of a year. According to the climatologists, Bangladesh has four seasons which are Monsoon, Post-monsoon, Summer, and Winter based on the geographical characteristics of this country. This paper aims to determine the best-fitted probability distribution model for the monthly rainfall data of each particular season in the Khulna district of Bangladesh using the rainfall data of the Khulna region from 1951 to 2018. Very commonly used seven continuous distributions- Normal, Weibull, Gamma, Log-normal, Exponential, Cauchy, and Logistic distributions were used to model the data and to evaluate the performances of the distributions, three non-parametric goodness-of-fit tests were conducted, and AIC, BIC values were calculated. Parameters of the distributions were estimated by the maximum likelihood method. The best-fit result of each season was taken as the distribution with the lowest AIC and BIC values. Among the seven distributions, the Gamma distribution showed the best-fit results of the monthly rainfall data for the Monsoon, Post-Monsoon, and Winter Season, and the Weibull distribution showed the best-fit result for Summer Season.

scholarly journals Regional Frequency Analysis of Maximum Monthly Rainfall in Haryana State of India Using L-Moments

2021 ◽  
Author(s):  
Mohit Nain ◽  
B. K. Hooda
Keyword(s):  
Return Period ◽  
Goodness Of Fit ◽  
Regional Distribution ◽  
Rain Gauge ◽  
Monthly Rainfall ◽  
Regional Frequency Analysis ◽  
Goodness Of Fit Test ◽  
Pearson Type ◽  
L Moments ◽  
Regional Frequency

The paper aims to select the appropriate regional frequency distribution for the maximum monthly rainfall and estimation of quantiles using L-moments for the 27 rain gauge stations in Haryana. These 27 rain gauge stations were grouped into three homogeneous regions (Region-1, Region-2, and Region-3) using Ward’s method of cluster analysis. To confirm the homogeneity of each region, L-moments based measure of heterogeneity was used. For each homogeneous region, a regional distribution was selected with the help of the L-moments ratio diagram and goodness-of-fit test. Results of the goodness-of-fit test and L-moments ratio diagram indicated that Generalized Logistic and Generalized Extreme Value distributions were best- fitted regional frequency distributions for the Region-1 and Region-2 respectively while for Region-3, Pearson Type-3) was best-fitted distribution. The quantiles for each region were calculated and the regional growth curves were developed. The accuracy measurements were determined using Monte Carlo simulations for the regional quantiles. Results of simulations showed that uncertainty in regional quantiles measured by Root Mean Square Error value and 90 percent error limits were small when the return period was low but uncertainty in quantiles increases as the return period increases.

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

scholarly journals Forecasting rainfall using soft computing techniques – A case study using India rainfall data

2019 ◽  
Vol 561 ◽  
pp. 012119
Author(s):  
Binoy B Nair ◽  
S Silamparasu ◽  
R Mohnish ◽  
T S Deepak ◽  
M Rahul
Keyword(s):  
Soft Computing ◽  
Rainfall Data ◽  
Soft Computing Techniques

scholarly journals Exploring the rainfall data from satellites to monitor rainfall induced landslides – A case study

2020 ◽  
Vol 66 (4) ◽  
pp. 887-894
Author(s):  
Manoj Kumar Thakur ◽  
Srinivas Desamsetti ◽  
A. Naga Rajesh ◽  
K. Koteswara Rao ◽  
M.S. Narayanan ◽  
...  
Keyword(s):  
Rainfall Data

scholarly journals Computational Modeling of Music Cognition: A Case Study on Model Selection

2006 ◽  
Vol 23 (5) ◽  
pp. 365-376 ◽  
Author(s):  
Henkjan Honing
Keyword(s):  
Model Selection ◽  
Strong Evidence ◽  
Goodness Of Fit ◽  
Computational Models ◽  
Recent Literature ◽  
Music Cognition ◽  
Kinematic Model ◽  
Music Performance ◽  
Limited Range

While the most common way of evaluating a computational model is to see whether it shows a good fit with the empirical data, recent literature on theory testing and model selection criticizes the assumption that this is actually strong evidence for the validity of a model. This article presents a case study from music cognition (modeling the ritardandi in music performance) and compares two families of computational models (kinematic and perceptual) using three different model selection criteria: goodness-of-fit, model simplicity, and the degree of surprise in the predictions. In the light of what counts as strong evidence for a model’s validity—namely that it makes limited range, nonsmooth, and relatively surprising predictions—the perception-based model is preferred over the kinematic model.

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