English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28603/40634
Visitors : 4354515      Online Users : 212
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40130

Title: Parameter automatic calibration approach for neural-network-based cyclonic precipitation forecast models
Authors: Der-Chang Lo
Chih-Chiang Wei
En-Ping Tsai
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: optimization
artificial neural network
parameter calibration
hydrology
Date: 2015-07
Issue Date: 2017-01-16T01:17:29Z
Publisher: Water
Abstract: Abstract: This paper presents artificial neural network (ANN)-based models for forecasting precipitation, in which the training parameters are adjusted using a parameter automatic calibration (PAC) approach. A classical ANN-based model, the multilayer perceptron (MLP) neural network, was used to verify the utility of the proposed ANN-PAC approach. The MLP-based ANN used the learning rate, momentum, and number of neurons in the hidden layer as its major parameters. The Dawu gauge station in Taitung, Taiwan, was the study site, and observed typhoon characteristics and ground weather data were the study data. The traditional multiple linear regression model was selected as the benchmark for comparing the accuracy of the ANN-PAC model. In addition, two MLP ANN models based on a trial-and-error calibration method, ANN-TRI1 and ANN-TRI2, were realized by manually tuning the parameters. We found the results yielded by the ANN-PAC model were more reliable than those yielded by the ANN-TRI1, ANN-TRI2, and traditional regression models. In addition, the computing efficiency of the ANN-PAC model decreased with an increase in the number of increments within the parameter ranges because of the considerably increased computational time, whereas the prediction errors decreased because of the model's increased capability of identifying optimal solutions.
Relation: 7(7)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40130
Appears in Collections:[海洋環境資訊系] 期刊論文

Files in This Item:

There are no files associated with this item.



All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback