文献
J-GLOBAL ID:202202220057119467
整理番号:22A0324491
中国における陸面土壌水分改善のためのマルチソース入力による機械学習モデルの開発【JST・京大機械翻訳】
Developing machine learning models with multisource inputs for improved land surface soil moisture in China
著者 (7件):
Wang Lei
(State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, PR China)
,
Wang Lei
(National Climate Center, China Meteorological Administration, Beijing 100081, PR China)
,
Fang Shibo
(State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, PR China)
,
Pei Zhifang
(School of Architecture, Nanyang Institute of Technology, Nanyang 473004, PR China)
,
Wu Dong
(College of Resources and Environment, Anhui Agricultural University, Hefei 230036, PR China)
,
Zhu Yongchao
(Meteorological Observation Center, China Meteorological Administration, Beijing 100081, PR China)
,
Zhuo Wen
(State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, PR China)
資料名:
Computers and Electronics in Agriculture
(Computers and Electronics in Agriculture)
巻:
192
ページ:
Null
発行年:
2022年
JST資料番号:
T0337A
ISSN:
0168-1699
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)