|
|
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.
|
|
|
Control of the reservoir storage function using artificial intelligence methods
Hon, Matěj ; BBA, Šárka Zemanová, (referee) ; Kozel, Tomáš (advisor)
The diploma thesis deals with flow prediction using artificial intelligence to control the storage function of the reservoir. It focuses on the control of storage function using combination of dispatching graphs and flow prediction. The work is divided into a methodological part and an application part. The methodological part contain describes how the acquisition of historical data, a description of the work of dispatching graphs and forecasting models. The application part contains flow forecasts and outflow control. A prediction model is based on the fuzzy method, and it is used to predict inflows. The calibration and validation of the prediction model is also described. Results of prediction model were evaluated. In next step the results of control method were evaluated and compared with result of dispatching graphs. The results of controlled method were satisfactory.
|
|
|
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.
|
|
|
Control of the reservoir storage function using artificial intelligence methods
Hon, Matěj ; BBA, Šárka Zemanová, (referee) ; Kozel, Tomáš (advisor)
The diploma thesis deals with flow prediction using artificial intelligence to control the storage function of the reservoir. It focuses on the control of storage function using combination of dispatching graphs and flow prediction. The work is divided into a methodological part and an application part. The methodological part contain describes how the acquisition of historical data, a description of the work of dispatching graphs and forecasting models. The application part contains flow forecasts and outflow control. A prediction model is based on the fuzzy method, and it is used to predict inflows. The calibration and validation of the prediction model is also described. Results of prediction model were evaluated. In next step the results of control method were evaluated and compared with result of dispatching graphs. The results of controlled method were satisfactory.
|
|
|
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.
|
guest ::
