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| Titel |
Evaluation of 1-D tracer concentration profile in a small river by means of Multi-Layer Perceptron Neural Networks |
| VerfasserIn |
A. Piotrowski, S. G. Wallis, J. J. Napiórkowski, P. M. Rowiński |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 11, no. 6 ; Nr. 11, no. 6 (2007-12-07), S.1883-1896 |
| Datensatznummer |
250009550
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| Publikation (Nr.) |
 copernicus.org/hess-11-1883-2007.pdf |
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| Zusammenfassung |
The prediction of temporal concentration profiles of a transported
pollutant in a river is still a subject of ongoing research efforts
worldwide. The present paper is aimed at studying the possibility of
using Multi-Layer Perceptron Neural Networks to evaluate the whole
concentration versus time profile at several cross-sections of a
river under various flow conditions, using as little information
about the river system as possible. In contrast with the earlier
neural networks based work on longitudinal dispersion coefficients,
this new approach relies more heavily on measurements of
concentration collected during tracer tests over a range of flow
conditions, but fewer hydraulic and morphological data are needed.
The study is based upon 26 tracer experiments performed in a small
river in Edinburgh, UK (Murray Burn) at various flow rates in a 540 m
long reach. The only data used in this study were concentration
measurements collected at 4 cross-sections, distances between the
cross-sections and the injection site, time, as well as flow rate
and water velocity, obtained according to the data measured at the
1st and 2nd cross-sections.
The four main features of concentration versus time profiles at a
particular cross-section, namely the peak concentration, the arrival
time of the peak at the cross-section, and the shapes of the rising
and falling limbs of the profile are modeled, and for each of them a
separately designed neural network was used. There was also a
variant investigated in which the conservation of the injected mass
was assured by adjusting the predicted peak concentration. The
neural network methods were compared with the unit peak attenuation
curve concept.
In general the neural networks predicted the main features of the
concentration profiles satisfactorily. The predicted peak
concentrations were generally better than those obtained using the
unit peak attenuation method, and the method with mass-conservation
assured generally performed better than the method that did not
account for mass-conservation. Predictions of peak travel time were
also better using the neural networks than the unit peak attenuation
method. Including more data into the neural network training set
clearly improved the prediction of the shapes of the concentration
profiles. Similar improvements in peak concentration were less
significant and the travel time prediction appeared to be largely
unaffected. |
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