中文版 | English

在结果中检索

IOCAS-IR

浏览/检索结果: 共15条，第1-10条 帮助

限定条件                    
已选(0)清除 条数/页：   排序方式：
A multi-model prediction system for ENSO 期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2023, 页码: 10
作者:  Liu, Ting;  Gao, Yanqiu;  Song, Xunshu;  Gao, Chuan;  Tao, Lingjiang;  Tang, Youmin;  Duan, Wansuo;  Zhang, Rong-Hua;  Chen, Dake
收藏  |  浏览/下载：63/0  |  提交时间：2023/12/13
MME  ENSO  Prediction  
Widespread global disparities between modelled and observed mid-depth ocean currents 期刊论文
NATURE COMMUNICATIONS, 2023, 卷号: 14, 期号: 1, 页码: 9
作者:  Su, Fenzhen;  Fan, Rong;  Yan, Fengqin;  Meadows, Michael;  Lyne, Vincent;  Hu, Po;  Song, Xiangzhou;  Zhang, Tianyu;  Liu, Zenghong;  Zhou, Chenghu;  Pei, Tao;  Yang, Xiaomei;  Du, Yunyan;  Wei, Zexun;  Wang, Fan;  Qi, Yiquan;  Chai, Fei
收藏  |  浏览/下载：97/0  |  提交时间：2023/12/13
Tropical cyclone over the western Pacific triggers the record-breaking '21/7' extreme rainfall in Henan, central-eastern China 期刊论文
ENVIRONMENTAL RESEARCH LETTERS, 2022, 卷号: 17, 期号: 12, 页码: 10
作者:  Yu, Yang;  Gao, Tao;  Xie, Lian;  Zhang, Rong-Hua;  Zhang, Wei;  Xu, Hongxiong;  Cao, Fuqiang;  Chen, Bin
收藏  |  浏览/下载：137/0  |  提交时间：2023/01/04
tropical cyclone  '21  7' extreme rainfall  Typhoon In-Fa  water vapor budget  
The encountering dune fields in a bidirectional flow system in the northwestern South China Sea: Pattern, morphology, and recent dynamics 期刊论文
GEOMORPHOLOGY, 2022, 卷号: 406, 页码: 14
作者:  Li, Jinyuan;  Yan, Jun;  Feng, Xingru;  Song, Yongdong;  Xu, Tao;  Zhuang, Lihua;  Luan, Zhendong;  Zhang, Jianxing;  Ma, Xiaochuan
Adobe PDF(14675Kb)  |  收藏  |  浏览/下载：112/0  |  提交时间：2022/07/18
Dune fields  Bidirectional flow  Morphology  Tide  the South China Sea  
Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images 期刊论文
JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2020, 卷号: 38, 期号: 5, 页码: 1435-1444
作者:  Zhang Xudong;  Li Xiaofeng;  Zhang Tao
Adobe PDF(15123Kb)  |  收藏  |  浏览/下载：172/0  |  提交时间：2021/04/12
internal waves (IWs)  Sulu Sea  Moderate-Resolution Imaging Spectroradiometer (MODIS)  Suomi National Polar-Orbiting Partnership (NPP)  
Research on radionuclide migration in coastal waters under nuclear leakage accident 期刊论文
PROGRESS IN NUCLEAR ENERGY, 2020, 卷号: 118, 页码: 9
作者:  Li, Zichao;  Zhou, Tao;  Zhang, Boya;  Si, Guangcheng;  Ali, Shahzad Muhammad
Adobe PDF(4436Kb)  |  收藏  |  浏览/下载：288/0  |  提交时间：2020/03/20
Nuclear leakage accident  Radionuclide  Coastal waters  Migration  Tide  Decay  
A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery 期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 59801-59820
作者:  Song, Dongmei;  Zhen, Zongjin;  Wang, Bin;  Li, Xiaofeng;  Gao, Le;  Wang, Ning;  Xie, Tao;  Zhang, Ting
Adobe PDF(5827Kb)  |  收藏  |  浏览/下载：201/0  |  提交时间：2020/09/23
Marine oil spill  RADARSAT-2  PolSAR  deep learning  feature extraction  convolutional neural network (CNN)  
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting 期刊论文
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2019, 卷号: 34, 期号: 5, 页码: 27
作者:  Zhang, Yang;  Cheng, Tao;  Ren, Yibin;  Xie, Kun
Adobe PDF(4145Kb)  |  收藏  |  浏览/下载：328/0  |  提交时间：2020/03/20
Short-term traffic forecasting  spatial-temporal dependency  network topology  graph convolution  residual long short-term memory  
A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes 期刊论文
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2019, 页码: 22
作者:  Ren, Yibin;  Chen, Huanfa;  Han, Yong;  Cheng, Tao;  Zhang, Yang;  Chen, Ge
Adobe PDF(3526Kb)  |  收藏  |  浏览/下载：305/0  |  提交时间：2019/11/14
Spatio-temporal flow volume  prediction  deep learning  LSTM  ResNet  
Model parameter-related optimal perturbations and their contributions to El Nino prediction errors 期刊论文
CLIMATE DYNAMICS, 2019, 卷号: 52, 期号: 3-4, 页码: 1425-1441
作者:  Tao, Ling-Jiang;  Gao, Chuan;  Zhang, Rong-Hua
Adobe PDF(6712Kb)  |  收藏  |  浏览/下载：200/0  |  提交时间：2019/05/15
Intermediate coupled model  CNOP approach  Model parameters  El Nino predictability