Hydrological Applications of Artificial Neural Networks
Duncan, Andrew
Date: 10 September 2010
Publisher
University of Exeter
Abstract
This paper looks at two example applications of Artificial Neural Networks (ANNs)
to hydrology. The first implements a Multi-Layer Perceptron (MLP) to correct
flow-rate simulations from the WRIP simulator (Han, 1991) for hourly
observations of a single flow-rate and to predict it up to 5-hours in ...
This paper looks at two example applications of Artificial Neural Networks (ANNs)
to hydrology. The first implements a Multi-Layer Perceptron (MLP) to correct
flow-rate simulations from the WRIP simulator (Han, 1991) for hourly
observations of a single flow-rate and to predict it up to 5-hours in advance.
Improvements in accuracy as compared with the WRIP output were clearly
demonstrated. The second implements a Multi-Layer Perceptron (MLP) to act as a
surrogate for the 3DNet / SIPSON simulator (University of Belgrade, 2010), which
uses a conventional fluid-dynamic approach to predict flooding surcharge levels in
sewer networks. An MLP-based data-driven model is created to emulate the
SIPSON outputs for a 123-manhole sub-network from Keighley, West Yorkshire,
UK under a range of rainstorm durations and return periods. A 3-minute sampling
period was used. Both a flood level analogue and a classification scheme were
successfully implemented. Early results show nowcasting predictive capability for
up to 30-minutes ahead.
Engineering
Faculty of Environment, Science and Economy
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