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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40177

Title: Soft computing techniques in ensemble precipitation nowcast
Authors: Chih-Chiang Wei
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: Forecast
Machine learning
Skill score
Rainfall
Date: 2013-02
Issue Date: 2017-01-16T03:08:22Z
Publisher: Applied Soft Computing
Abstract: Abstract: This study presented various soft computing techniques for forecasting the hourly precipitations during tropical cyclones. The purpose of the current study is to present a concise and synthesized documentation of the current level of skill of various models at precipitation forecasts. The techniques involve artificial neural networks (ANN) comprising the multilayer perceptron (MLP) with five training methods (denoted as ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5), and decision trees including classification and regression tree (CART), Chi-squared automatic interaction detector (CHAID), and exhaustive CHAID (E-CHAID). The developed models were applied to the Shihmen Reservoir Watershed in Taiwan. The traditional statistical models including multiple linear regressions (MLR), and climatology average model (CLIM) were selected as the benchmarks and compared with these machine learning. A total of 157 typhoons affecting the watershed were collected. The measures used include numerical statistics and categorical statistics. The RMSE criterion was employed to assess the suitable scenario, while the categorical scores, bias, POD, FAR, HK, and ETS were based on the rain contingency table. Consequently, this study found that ANN and decision trees provide better prediction compared to traditional statistical models according to the various average skill scores.
Relation: 13(2)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40177
Appears in Collections:[海洋環境資訊系] 期刊論文

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