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| Titel |
Neural network modelling of sediment-discharge relationships: Pictorial analysis of six computational methodologies applied to two rivers in Missouri |
| VerfasserIn |
N. Ab Ghani, R. J. Abrahart, N. J. Clifford |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250019275
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| Zusammenfassung |
| Neural networks can be trained to model the sediment-discharge relationship: numerous
illustrative applications exist. The standard method of reporting involves using a
scatterplot of observed versus predicted records, plus a handful of global statistics, to
support an assessment of model skill. This traditional approach will nevertheless
result in undesirable side effects since it reinforces the ’black box’ criticisms and
associated demonisation that is sometimes levelled at computational intelligence
solutions: no ’line-of-best-fit’ is ever supplied. This paper in contrast compares
and evaluates six computational methods for modelling the sediment-discharge
relationship from a structural and behavioural standpoint in which the exact nature of
each model is visualised for the purposes of diagnostic appraisal and scientific
enlightenment. The following methods are compared: backpropagation neural network;
corrected power function; simple linear regression; piecewise linear regression using an
M5 Model Tree; LOWESS; and Robust LOWESS. Modelling is restricted to a
consideration of bivariate relationships. The models were developed on daily river
discharge and sediment concentration datasets for two rivers in Missouri: Lower
Salt River and Little Black River. Each dataset was divided into two parts using
different methods and each model was first calibrated on one sub-set and thereafter
tested on the other. The datasets were next swapped over and the process repeated.
Each model is also evaluated using statistical measures calculated in HydroTest
(http://www.hydrotest.org.uk/). The need for more benchmarking exercises of a similar nature
is highlighted. |
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