Improved back-propagation networks for reservoir inflow forecasting
Date Issued
2008
Date
2008
Author(s)
Jeng, Jia-Haur
Abstract
The efficiency is an important issue for neural networks-based models, but the issue has received little attention in the hydrologic domain. Back-propagation networks (BPNs) are the most frequently used convectional neural networks (NNs). However, BPNs are trained by the error back-propagation algorithm which is a very time-consuming iterative process. To improve the efficiency, improved BPNs which are trained by a novel query learning approach are proposed. The proposed query learning approach is capable of selecting informative data from all training data. Then the improve BPNs can be efficiently trained with partial data. An application is conducted to demonstrate the superiority of the improved BPNs. Two kinds of BPN-based (the improved and the conventional BPN-based) reservoir inflow forecasting models are constructed and the comparison between the improved and the conventional BPN-based model is made. The results show that the performance of the improved BPN-based models is as good as that of the conventional BPN-based models, but the improved BPN-based models significantly required less training time than the conventional BPN-based models. As compared to the conventional BPN models, only about 50% of training time is required for the improved BPN-based models. The improved BPN-based models are recommended as an alternative to the existing models because of their efficiency.
Subjects
neural networks
back-propagation neural networks
query learning approach
reservoir inflow forecasting
Type
thesis
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