National Taiwan Ocean University Institutional Repository:Item 987654321/40145
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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40145

Title: Comparing lazy and eager learning models for water level forecasting in river-reservoir basins of inundation regions
Authors: Chih-Chiang Wei
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: Basin
Eager learning
Lazy learning
Prediction
Water level
Date: 2015-01
Issue Date: 2017-01-16T01:43:03Z
Publisher: Environmental Modelling & Software
Abstract: Abstract
This study developed a methodology for formulating water level models to forecast river stages during typhoons, comparing various models by using lazy and eager learning approaches. Two lazy learning models were introduced: the locally weighted regression (LWR) and the k-nearest neighbor (kNN) models. Their efficacy was compared with that of three eager learning models, namely, the artificial neural network (ANN), support vector regression (SVR), and linear regression (REG). These models were employed to analyze the Tanshui River Basin in Taiwan. The data collected comprised 50 historical typhoon events and relevant hourly hydrological data from the river basin during 1996–2007. The forecasting horizon ranged from 1 h to 4 h. Various statistical measures were calculated, including the correlation coefficient, mean absolute error, and root mean square error. Moreover, significance, computation efficiency, and Akaike information criterion were evaluated. The results indicated that (a) among the eager learning models, ANN and SVR yielded more favorable results than REG (based on statistical analyses and significance tests). Although ANN, SVR, and REG were categorized as eager learning models, their predictive abilities varied according to various global learning optimizers. (b) Regarding the lazy learning models, LWR performed more favorably than kNN. Although LWR and kNN were categorized as lazy learning models, their predictive abilities were based on diverse local learning optimizers. (c) A comparison of eager and lazy learning models indicated that neither were effective or yielded favorable results, because the distinct approximators of models that can be categorized as either eager or lazy learning models caused the performance to be dependent on individual models.
Relation: 63
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40145
Appears in Collections:[Department of Marine Environmental Informatics] Periodical Articles

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