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PCA-based SVM classification for simulated ice floes in front of sluice gates
https://nipr.repo.nii.ac.jp/records/16989
https://nipr.repo.nii.ac.jp/records/16989b41326d5-d6ef-4f21-92d3-3898fb739222
Item type | 学術雑誌論文 / Journal Article(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2022-12-01 | |||||
タイトル | ||||||
タイトル | PCA-based SVM classification for simulated ice floes in front of sluice gates | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | PCA-based SVM classification for simulated ice floes in front of sluice gates | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Ice floes | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Sluice gate | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Classification | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | PCA-SVM | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Machine learning | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
著者 |
Liang, Naisheng
× Liang, Naisheng× Tuo, Youcai× Deng, Yun× He, Tianfu |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | The entrainment and accumulation of ice floes in front of sluice gates are closely related to the water transport efficiency and safe operation of the channel during the ice period. A flume study is carried out for a sluice gate with free outflow. An integrated principal component analysis and support vector machine (PCA-SVM) model for simulated ice floes classification is proposed. Based on the mechanism of ice floe accumulation, ten input characteristics of the model are selected. The first principal component, with a contribution rate of 71.76%, and the second principal component, with a contribution rate of 15.64%, are extracted as the inputs of the SVM model. The 5-fold cross-validation method is used to examine the model. The training results show that the Gaussian radial basis function (RBF) is the optimal kernel function. The performance of the developed model is measured by a confusion matrix and receiver operating characteristic (ROC) analysis. The results show that the established PCA-SVM model improves upon the Bernoulli naive Bayes (Bernoulli NB) and K-nearest neighbor (KNN) models, increasing the area under the ROC curve (AUC) values by 11% and 5%, the accuracy (Acc) values by 16% and 17%, and the F1 values by 17% and 2%, respectively. | |||||
書誌情報 |
en : Polar Science p. 100839, 発行日 2022-12 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 18739652 | |||||
DOI | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1016/j.polar.2022.100839 | |||||
関連名称 | 10.1016/j.polar.2022.100839 |