http://www.cnr.it/ontology/cnr/individuo/prodotto/ID41623
Genetic algorithm based discharges estimation ai sites receiving lateral inflows (Articolo in rivista)
- Type
- Label
- Genetic algorithm based discharges estimation ai sites receiving lateral inflows (Articolo in rivista) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1061/?ASCE?HE.1943-5584.0000009 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Tayfur G., Barbetta S., Moramarco T. (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1Prof., Dept. Civil Engineering; Izmir Institute of Technology, Urla, Izmir, 35340,Turkey. gokmentayfur@iyte.edu.tr , *Corresponding author
2Eng., Researcher, Research Institute for Hydrogeological Protection, National Research Council, Via Madonna Alta, 126, 06128 Perugia, Italy. s.barbetta@irpi.cnr.it
3Dr., Research Scientist, Research Institute for Hydrogeological Protection, National Research Council, Via Madonna Alta, 126, 06128 Perugia, Italy. T.Moramarco@irpi.cnr.it (literal)
- Titolo
- Genetic algorithm based discharges estimation ai sites receiving lateral inflows (literal)
- Abstract
- The genetic algorithm ?GA? technique is applied to obtain optimal parameter values of the standard rating curve model ?RCM?
for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant lateral inflows. The standard RCM uses
the information of discharge and effective cross-sectional flow area at an upstream station and effective cross-sectional flow area wave
travel time later at a downstream station to predict the flow rate at this last site. The GA technique obtains the optimal parameter values
of the model, here defined as the GA-RCM model, by minimizing the mean absolute error objective function. The GA-RCM model was
tested to predict hydrographs at three different stations, located on the Upper Tiber River in central Italy. The wave travel times
characterizing the three selected river branches are, on the average, 4, 8, and 12 h. For each river reach, seven events were employed, four
for the model parameters' calibration and three for model testing. The GA approach, employing 100 chromosomes in the initial gene pool,
75% crossover rate, 5% mutation rate, and 10,000 iterations, made the GA-RCM model successfully simulate the hydrographs observed
at each downstream section closely capturing the trend, time to peak, and peak rates with, on the average, less than 5% error. The model
performance was also tested against the standard RCM model, which uses, on the contrary to the GA-RCM model, different values for the
model parameters and wave travel time for each event, thus, making the application of the standard RCM for real time discharge
monitoring inhibited. The comparative results revealed that the RCM model improved its performance by using the GA technique in
estimating parameters. The sensitivity analysis results revealed that at most two events would be sufficient for the GA-RCM model to
obtain the optimal values of the model parameters. A lower peak hydrograph can also be employed in the calibration to predict a higher
peak hydrograph. Similarly, a shorter travel time hydrograph can be used in GA to obtain optimal model parameters that can be used to
simulate floods characterized by longer travel time. For its characteristics, the GA-RCM model is suitable for the monitoring of discharge
in real time, at river sites where only water levels are observed. (literal)
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