Improved Self-organizing Linear Output Map for Reservoir Inflow Forecasting
Date Issued
2009
Date
2009
Author(s)
Chang, Chia-Chuang
Abstract
Based on self-organizing linear output map (SOLO), effective hourly reservoir inflow forecasting models are proposed. As compared with back-propagation neural network (BPNN) which is the most frequently used conventional neural network (NN), SOLO has four advantages: (1) SOLO has better generalization ability; (2) the architecture of the SOLO is simpler; (3) SOLO is trained much more rapidly, and (4) SOLO could provide features that facilitate insight into underlying processes. An application is conducted to clearly demonstrate these four advantages. The results indicate that the SOLO model is more well-performed and efficient than the existing BPN-based models. To further improve the peak inflow forecasting, SOLO with data preprocessing named ISOLO is also proposed. The comparison between SOLO and ISOLO confirms the significant improvement in peak inflow forecasting. The proposed model is recommended as an alternative to the existing models. The proposed modeling technique is also expected to be useful to support reservoir operation systems.
Subjects
self-organizing linear output map
reservoir inflow forecasting
self-organizing map
peak inflow forecasting
data preprocessing
neural network
Type
thesis
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