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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40186

Title: Decision tree-based classifier combined with neural-based predictor for water-stage forecasts in a river basin during typhoons: A case study in Taiwan
Authors: Chia-Cheng Tsai
Mi-Cheng Lu
Chih-Chiang Wei
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: neural network
water stage
decision tree
Date: 2012-02
Issue Date: 2017-01-16T03:23:56Z
Publisher: Environmental Engineering Science
Abstract: Abstract: To solve the complicated problem of water-stage predictions under the interaction of upstream flows and tidal effects during typhoon attacks, this article presents a novel approach to river-stage predictions. The proposed CART-ANN model combines both the decision trees (classification and regression trees [CART]) and the artificial neural network (ANN) techniques, which comprise the multilayer perceptron (MLP) and radial basis function (RBFNN). The combined CART-ANN model involves a two-step predicting process. First, the CART stage-level classifier can classify the river stages into higher, middle, and lower levels. Then, the ANN-based water-stage predictors are employed to predict the water stages. The proposed model was applied to the Tanshui River Basin in Taiwan. The Taipei Bridge, which is close to the estuary and affected by tidal effects, was taken as the study gauge. The mean square error and the mean absolute error were used for evaluating the variance and bias performances of the models. This study makes two contributions. First, the CART-MLP and CART-RBF were modeled to predict river stages under tidal effects during typhoons, and they were compared with three benchmark models, CART, back-propagation neural network, and RBFNN. Second, the CART-RBF successfully demonstrated that it achieved more accurate prediction than CART-MLP and three benchmark models.
Relation: 29(2)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40186
Appears in Collections:[海洋環境資訊系] 期刊論文

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