Study on Improved Neural Network Approaches in Hydrosystem
Date Issued
2005
Date
2005
Author(s)
Chen, Guo-Rong
DOI
en-US
Abstract
Artificial neural networks (ANNs) have found increasing applications in various aspects of hydrology and previous studies have shown the potential of ANNs for modeling hydrological processes. However, ANN models failed to be applied to some hydrological problems, because the ANN architectures are usually lack of physical mechanisms. In addition, the ANN models were constructed by a trial and error procedure, which requires amount of time. Hence applications of ANNs in hydrology cry for approaches to the construction of ANN models, which are capable of improving the performance of ANN models. The object of this thesis is to establish effective approaches to the construction of ANN models in different problems of water resources and hydrology.
In this thesis, two concepts for constructing ANN models in hydrology are presented. The first concept is to construct ANN models based on known physical mechanisms and the second concept is to construct adequate ANN models only included highly relevant inputs. In Chapters 2 and 3, two ANN approaches, Back-propagation neural networks (BPNs) and radial basis function networks (RBFNs) approaches, based on the first concept are established to determine aquifer parameters from pumping test data. The major difference between the existing and the proposed ANN approaches is the design of ANN input and output components. The proposed ANNs are designed according to the analytical solutions, which express known physical mechanisms. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data demonstrates that our design of ANNs is better than the existing ANN approach.
In Chapter 4, a systematic approach based on the second concept is used to construct ANN rainfall-runoff models. In order to construct adequate ANN models only included highly relevant inputs, the irrelevant inputs will be trimmed by the systematic approach. An application to the Fei-Tsui Reservoir Watershed in northern Taiwan shows that the proposed ANN rainfall-runoff model has advantages over those obtained by the trial and error procedure. The proposed approaches will be helpful to hydrologist to construct adequate ANN-based hydrological models.
Subjects
降雨-逕流模式
地下水含水層參數檢定
幅射基底函數網路
倒傳遞類神經網路
類神經網路
Artificial neural networks
The determination of aquifer parameters
Rainfall-runoff model
Back-propagation neural networks
Radial basis function networks
Type
thesis
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