An artificial neural network approach to rainfall-runoff modelling
作者:
CHRISTIANW. DAWSON,
ROBERT WILBY,
期刊:
Hydrological Sciences Journal
(Taylor Available online 1998)
卷期:
Volume 43,
issue 1
页码: 47-66
ISSN:0262-6667
年代: 1998
DOI:10.1080/02626669809492102
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in two flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.
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