|
International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 7, No. 2, 2010, pp. 215-224
|
Bioline Code: st10021
Full paper language: English
Document type: Research Article
Document available free of charge
|
|
International Journal of Environment Science and Technology, Vol. 7, No. 2, 2010, pp. 215-224
en |
Forecast of water level and ice jam thickness using the back propagation neural network and support vector machine methods
Wang, J.; Sui, J.; Guo, L.; Karney, B. W. & Jüpner, R.
Abstract
Ice jams can sometimes occur in high latitude rivers during winter and the resulting water level rise may
generate costly and dangerous flooding such as the recent ice jam flooding in the Nechako River in downtown Prince
George in Canada. Thus, the forecast of water level and ice jam thickness is of great importance. This study compares
three methods to simulate and forecast water level and ice jam thickness based on field observations of river ice jams in
the Quyu Reach of the Yellow River in China. More specifically, simulation results generated by the traditional multivariant
regressional method are compared to those of the back propagation neural network and the support vector
machine methods. The forecast of ice jam thickness and water level under ice jammed condition have been conducted in
two different approaches, 1) simulation of water level and ice jam thickness in the second half of the period of
measurement using models developed based on data gained during the first half of the period of measurement, 2)
simulation of water level and ice jam thickness at the downstream cross sections using models developed based on data
gained at the upstream cross sections. For this reason, as the results of simulation and field observations indicated, the
back propagation neural network method and the support vector machine method are superior in terms of accuracy to
the multi-variant regressional method.
Keywords
Ice jam thickness; Multi-variant regressional method; Water level
|
|
© Copyright 2010 - Center for Environment and Energy Research and Studies (CEERS) Alternative site location: http://www.ijest.org
|
|