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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40190

Title: Nearest neighbor search for diagnosing rain/non-rain discrimination
Authors: Chih Chiang Wei
Yu Hui Lu
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Keywords: Receiver Operating Characteristic
Classification
Nearest Neighbor Search
Rain
Date: 2012-11
Issue Date: 2017-01-16T03:33:50Z
Publisher: Advanced Materials Research
Abstract: Abstract: This study examines the rain occurrence by the passive microwave imagery during typhoons. The dataset consists of 53 typhoons affecting the watershed over 2001-2008. This study employs nearest neighbor search (NNS) classifier which is often used for diagnosing forecast problems. The multilayer perceptron (MLP) and logistic regressions (LR) are selected as the benchmarks. The results show that for the rain/non-rain discrimination, the best performing classifier is NNS according to the AUC measures. The results show that the use of NNS can effectively improve the AUC measures for diagnosing rain occurrence. Overall, the use of NNS is a relatively effective algorithm comparing to other classifiers.
Relation: 599
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40190
Appears in Collections:[海洋環境資訊系] 期刊論文

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