Incorporated Artificial Neural Networks for Estimating Hydrological Events

• Main Authors: Yu-Feng Ting, 丁裕峰
• Other Authors: 張斐章
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/67546898602041450664

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 92 === 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.