Use BPN to predict river’s water level of the fixing point
Date Issued
2006
Date
2006
Author(s)
Bau, Ji-Yung
DOI
zh-TW
Abstract
In the past, the focus of flood forecasting model is on the flowrate in the river. It is unfortunate that the field measurements of the flowrate are not very accurate. Futhermore, the discharge measurement has to converted to water level by using the flowrate-level rating curve. It is not convenient and reliable to use the predicted water level in the flood warning system. It is intended to predict water level at a outlet of a watershed, to prouide upstream boundary condition, for river flood forecasting model. The technique of back-propagation neural network is employed to establish relationship between prior hydrological observations and water level. The developed relationship is then used for water level prediction when hydrological information is provided.
Tradictionally, the concept of concertration time of runoff in a watershed is assumed to relate future runoff flowrate to prior hydrological data. Based on the time-step hydrological input, the watershed runoff can be obtained. This is a tedious process and not reliable. In this paper, correlation analysis are performed to determined the highest correlation coefficient of prior hydrological events. The neural network technique is then employed to develope a prediction model for water level at the outlet of a watershed. It is demonstrated to be more efficient and consistent then tranditional approach. The advantages are illustrated by using historical events. Better perdiction accuracy is observed.
Subjects
倒傳遞類神經網路
相關性分析
back-propagation neural network
correlation analysis
Type
thesis
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