Investigating Watershed Multistep Rainfall-Runoff Mechanisms and Modeling Flood Forecasting by Artificial Neural Networks
Date Issued
2009
Date
2009
Author(s)
Ho, Yi-Hua
Abstract
Reservoir operation is a prerequisite component for water resource management during typhoon periods. Due to the floods caused by typhoons often reach the reservoir within few hours, the need for a higher reliability of reservoir operation strategies is required. Therefore, an accurate hydrological model plays a key role and provides valuable flood information for reservoir operational decisions. The major purpose of this study is to construct a stable and reliable multistep ahead inflow forecasting of Shihmen reservoir. First of all, the characteristics of rainfall-runoff processes in this watershed are investigated by using trend detection methods for finding the relation between rainfall and runoff. In the second part, the artificial neural network (ANN), an effective data manipulation and prediction tool, is introduced in this study. The Adaptive Network-based Fuzzy Inference System (ANFIS) with model inputs consist of previous precipitation and streamflow information is developed for multistep ahead flood forecasting. The results indicate that both precipitation and flood patterns have the same trend with a shift of 5-7 hours. Accordingly, the ANFIS model provides accurate and effective flood forecasts to 5 hours-ahead. As far as the multistep ahead flood forecasting is concerned, appropriate combination of precipitation information indeed help to increase the accuracy of flood predictions.
Subjects
Rainfall-runoff modeling
Adaptive Network-based Fuzzy Inference System
Artificial neural network
Multistep ahead flood forecasting
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R96622001-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):dcf1093b5e66e2be26effe4e4c9c317a