文献
J-GLOBAL ID:202202279640486234
整理番号:22A1174552
深層学習,サポートベクトルマシンおよびロジスティック回帰を用いた地震時地滑り感受性マッピングのための新しいマークベースサンプリング戦略の有効性【JST・京大機械翻訳】
Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression
著者 (7件):
Xi Chuanjie
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
,
Han Mei
(School of Mathematics, Southwest Jiaotong University, Chengdu, China)
,
Hu Xiewen
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
,
Liu Bo
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
,
He Kun
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
,
Luo Gang
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
,
Cao Xichao
(Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China)
資料名:
Bulletin of Engineering Geology and the Environment
(Bulletin of Engineering Geology and the Environment)
巻:
81
号:
5
ページ:
174
発行年:
2022年
JST資料番号:
W4126A
ISSN:
1435-9529
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
ドイツ (DEU)
言語:
英語 (EN)