Incorporated Artificial Neural Networks for Estimating Hydrological Events
Date Issued
2004
Date
2004
Author(s)
Ting, Yu-Feng
DOI
zh-TW
Abstract
Accurate predicting hydrological event’s variation remains one of the most important and challenging tasks. Artificial neural network (ANN) is described as an information process system that consists of many nonlinear and densely interconnected processing units. With this parallel-distributed processing architecture, ANN has proven to be an efficient way for hydrological modeling and widely used for flood forecasting.
Each neural network has its own character and suitability for different data structures. For modeling a complex physical mechanisms such as watershed rainfall-runoff process, a single architecture of artificial neural network is hard, if not impossible, to make good descriptions for different hydrological characters and to maintain highly applicable forecasting system.
This research proposes an Incorporated Artificial Neural Network (IANN), which combines two ANN architectures for modeling the rainfall-runoff processes. The proposed method is compared with two famous ANN, the unsupervised learning Self-Organizing Map (SOM) and supervised learning Conjugate Gradient Back-Propagation (CGBP), by using Lan-Yan River data sets. Our goal is to find out the most suitable ANN for different hydrological events and to integrate the advantage of different kinds of ANN for improving the flood forecast ability.
To demonstrate the applicability and capability of the proposed IANN structure, the Lan-Yan river, Taiwan, was used as a case study. For the purpose of comparison, three kinds of ANNs (SOM, BP, and the incorporated ANN) were performed. The results show that Incorporated Artificial Neural Network has superior performance than any other single artificial neural networks and can accurately make an one-step and two-step ahead flood forecast.
Subjects
流量推估
併聯式類神經網路
倒傳遞類神經網路
自組織映射圖
CGBP
SOM
Incorporated Artificial Neural Network
Type
thesis
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