文献
J-GLOBAL ID:202202258005081529
整理番号:22A0161938
適切な隣接部位の特徴を含むことは長期短期記憶神経回路網モデルによるPM_2.5濃度予測を改善する【JST・京大機械翻訳】
Including the feature of appropriate adjacent sites improves the PM2.5 concentration prediction with long short-term memory neural network model
著者 (9件):
Mengfan Teng
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
Siwei Li
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
Siwei Li
(State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China)
,
ge Song
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
jie Yang
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
jie Yang
(State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China)
,
Lechao Dong
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
hao Lin
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
,
Senlin Hu
(School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China)
資料名:
Sustainable Cities and Society
(Sustainable Cities and Society)
巻:
76
ページ:
Null
発行年:
2022年
JST資料番号:
W2908A
ISSN:
2210-6707
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)