文献
J-GLOBAL ID:202202233958111344
整理番号:22A0856552
池における正確な短期および長期溶存酸素予測のためのハイブリッドXGBoost-ISSA-LSTMモデル【JST・京大機械翻訳】
A hybrid XGBoost-ISSA-LSTM model for accurate short-term and long-term dissolved oxygen prediction in ponds
著者 (12件):
Wu Yuhan
(National Innovation Center for Digital Fishery, China Agricultural University, Beijing, People’s Republic of China)
,
Wu Yuhan
(Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, China)
,
Wu Yuhan
(College of Information and Electrical Engineering, China Agricultural University, Beijing, China)
,
Sun Longqing
(National Innovation Center for Digital Fishery, China Agricultural University, Beijing, People’s Republic of China)
,
Sun Longqing
(Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, China)
,
Sun Longqing
(College of Information and Electrical Engineering, China Agricultural University, Beijing, China)
,
Sun Xibei
(National Innovation Center for Digital Fishery, China Agricultural University, Beijing, People’s Republic of China)
,
Sun Xibei
(Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, China)
,
Sun Xibei
(College of Information and Electrical Engineering, China Agricultural University, Beijing, China)
,
Wang Boning
(National Innovation Center for Digital Fishery, China Agricultural University, Beijing, People’s Republic of China)
,
Wang Boning
(Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, Beijing, China)
,
Wang Boning
(College of Information and Electrical Engineering, China Agricultural University, Beijing, China)
資料名:
Environmental Science and Pollution Research
(Environmental Science and Pollution Research)
巻:
29
号:
12
ページ:
18142-18159
発行年:
2022年
JST資料番号:
W4325A
ISSN:
0944-1344
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)