中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
地理科学与资源研究所 [5]
采集方式
OAI收割 [5]
内容类型
期刊论文 [5]
发表日期
2024 [1]
2023 [1]
2022 [1]
2020 [2]
学科主题
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Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau
期刊论文
OAI收割
EARTHS FUTURE, 2024, 卷号: 12, 期号: 3, 页码: e2024EF004417
作者:
Ren, Zhihui
;
Sang, Yan-Fang
;
Cui, Peng
;
Chen, Deliang
;
Zhang, Yichi
  |  
收藏
  |  
浏览/下载:39/0
  |  
提交时间:2024/04/08
sub-daily precipitation
databases
temporal scale
natural disasters
Qinghai-Tibet Plateau
high mountain regions
Spatio-temporal heterogeneous graph using multivariate earth observation time series: Application for drought forecasting
期刊论文
OAI收割
COMPUTERS & GEOSCIENCES, 2023, 卷号: 179, 页码: 13
作者:
Balti, Hanen
;
Ben Abbes, Ali
;
Sang, Yanfang
;
Mellouli, Nedra
;
Farah, Imed Riadh
  |  
收藏
  |  
浏览/下载:24/0
  |  
提交时间:2023/10/16
Heterogeneous graphs
Spatiotemporal data
Multivariate time series
Earth observation data
Drought forecasting
Multidimensional architecture using a massive and heterogeneous data: Application to drought monitoring
期刊论文
OAI收割
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2022, 卷号: 136, 页码: 1-14
作者:
Balti, Hanen
  |  
收藏
  |  
浏览/下载:37/0
  |  
提交时间:2022/12/20
Big data
Data storage
Spatio-temporal querying
Decision-making
Earth observation
Disaster management
A review of drought monitoring with big data: Issues, methods, challenges and research directions
期刊论文
OAI收割
ECOLOGICAL INFORMATICS, 2020, 卷号: 60, 页码: 17
作者:
Balti, Hanen
;
Ben Abbes, Ali
;
Mellouli, Nedra
;
Farah, Imed Riadh
;
Sang, Yanfang
  |  
收藏
  |  
浏览/下载:18/0
  |  
提交时间:2021/03/16
Drought monitoring
Artificial intelligence
Big data
Machine learning
Statistical approach
Remote sensing
A review of drought monitoring with big data: Issues, methods, challenges and research directions
期刊论文
OAI收割
ECOLOGICAL INFORMATICS, 2020, 卷号: 60, 页码: 17
作者:
Balti, Hanen
;
Ben Abbes, Ali
;
Mellouli, Nedra
;
Farah, Imed Riadh
;
Sang, Yanfang
  |  
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2021/03/16
Drought monitoring
Artificial intelligence
Big data
Machine learning
Statistical approach
Remote sensing