National Repository of Grey Literature 5 records found  Search took 0.00 seconds. 
Relation Between River Flows and Suspended Sediments in the Selected Hydrometric Profile of the Thaya River Basin
Hudíková, Dominika ; Menšík, Pavel (referee) ; Marton, Daniel (advisor)
The issue of the sediments of their transport and subsequent storage in water basins is an important topic which is perceived very seriously by the water management expert public. Therefore, work in this area is very desirable. The aim of bachelor‘s work is to find and estimate possible dependencies between average daily discharge and average daily values of non dissolved substances carried in suspension. The dependencies will serve as a partial basis for addressing the ATCZ28 SEDECO Sediments project, ecosystem services and interrelation with floods and droughts in the AT-CZ Border region. The data are analysed in different periods of time. The result of the analysis is the trend line regulations and the degree of dependence between flows and concentrations, which is expressed by means of a coefficient of determination. The practical application is performed on data measured in the Trávní Dvůr profile on the Dyje river for the years 1996 and 1997.
Regression goodness-of-fit criteria according to dependent variable type
Šimsa, Filip ; Hanzák, Tomáš (advisor) ; Hlubinka, Daniel (referee)
This work is devoted to the description of linear, logistic, ordinal and multinominal regression models and interpretation of its parameters. Then it introduces a variety of quality indicators of mathematical models and the re- lations between them. It focuses mainly on the Gini coefficient and the coefficient of determination R2 . The first mentioned is established by modifying the Lorenz curve for ordinal and continuous variables and by comparing the estimated proba- bilities for nominal variable. The coefficient of determination R2 is newly defined for the nominal variable and is examined its relationship with Gini coefficient. As- suming normally distributed scores and errors of the model is numerically derived the relation between the Gini coefficient and the coefficient of determiantion for different distribution of continuous dependent variable. Theoretical calculations and definitions are illustrated on two real data sets. 1
Estimation and goodness-of-fit criteria in logistic regression model
Ondrušková, Markéta ; Hanzák, Tomáš (advisor) ; Zvára, Karel (referee)
In this bachelor thesis we describe binary logistic regression model and estimation of model's parameters by maximum likelihood method. Then we propose algorithm for the least squares method. In the goodness-of-fit criteria part we define Lorenz curve, Gini coefficient, C-statistics, Kolmogorov-Smirnov statistics and coefficient of determination R2 . We derive their relation to different sample coefficients of correlation. We derive typical relation between Gini coeffi- cient, Kolmogorov-Smirnov statistics and newly also coefficient of determination R2 via model of normally distributed score of bad and good clients. These derived teoretical results are verified on three real data sets. Keywords: Binary logistic regression, maximum likelihood, ordinary least squa- res, Gini coefficient, coefficient of determination. 1
Stochastic Prediction of Mean Monthly Flows in Selected Hydrometric Profile
Jansa, Jakub ; Menšík, Pavel (referee) ; Marton, Daniel (advisor)
The diploma thesis is focused on the average monthly flows forecast in the selected hydrometric profile. Aim of this work will be evaluation of the calculated values and the interpretation of the results in understandable form. The next step will be find an appropriate connection between randomly-generated inputs in the form of random real flow series using the standard hydrological prediction models. This models are based on the principles of artificial intelligence and probability model. The result of the work will be verification of procedures and compilation of mean monthly flow stochastic forecast in selected hydrometric profile, which would be used for a reservoirs management, respectively for water systems management.
Long Term Discharge Prediction in River Hydrometric Profile
Šelepa, Milan ; Menšík, Pavel (referee) ; Marton, Daniel (advisor)
The diploma thesis is focused on the long term prediction of mean monthly flows in hydrometric profile for purposes of reservoir control optimization and optimization of reservoir systems. Discharges were predicted using by artificial neural network method. Predicted flows were statistically evaluated by relevant coefficients and then compared with the measured flows for given river hydrometric profiles.

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