Water Stage Forecasting of the Tanshui River under Tidal Effects by Using Neural Network
Resource
農業工程學報,47(4),29-38
Journal
農業工程學報
Journal Volume
47
Journal Issue
4
Pages
29-38
Date Issued
2001-12
Date
2001-12
Author(s)
Abstract
To forecast the water stage in open-channel under tidal effects is always a tough task. Even those sophisticatedly conceptual and mathematical models cannot do a good job. In this study we propose an artificial neural network model to forecast the one-hour-ahead water stage. One of the advantages of the artificial neural network is its powerful learning ability. During the training scheme, the training data with same similarities are clustered together at the beginning. Then the least squares method is used to estimate the weights of the model. Thus it can reduces the complexity of the system. The water stage data of the Tanshui River under tidal effects are used to construct a water stage forecasting model. The data is split into three independent subsets, namely, the training, validation, and testing subsets. The training subset is used for parameter estimation and model development. The validation subset is applied to choose the best model from the candidate ones. The testing subset is devoted to show the performance of the selected model. The results show that the artificial neural network model is a reliable and accurate tool for forecasting the water stage in an open-channel under tidal effects.
Subjects
Tidal effect
Artificial neural network
Water stage
Cluster
Type
journal article
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