A review of drought monitoring with big data: Issues, methods, challenges and research directions
文献类型：期刊论文
| 作者 | Balti, Hanen1,2; Ben Abbes, Ali1,4; Mellouli, Nedra2; Farah, Imed Riadh1,5; Sang, Yanfang3; Lamolle, Myriam2 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2020-11-01 |
| 卷号 | 60页码:17 |
| 关键词 | Drought monitoring Artificial intelligence Big data Machine learning Statistical approach Remote sensing |
| ISSN号 | 1574-9541 |
| DOI | 10.1016/j.ecoinf.2020.101136 |
| 通讯作者 | Balti, Hanen(hanen.balti@ensi-uma.tn) ; Sang, Yanfang(sangyf@igsnrr.ac.cn) |
| 英文摘要 | Over recent years, the frequency and intensity of droughts have increased and there has been a large drying trend over many parts of the world. Consequently, drought monitoring using big data analytic has gained an explosive interest. Droughts stand among the most damaging natural disasters. It threatens agricultural production, ecological environment, and socio-economic development. For this reason, early warning, accurate evaluation, and efficient prediction are an emergency especially for the nations that are the most menaced by this danger. There are numerous emerging studies addressing big data and its applications in drought monitoring. In fact, big data handle data heterogeneity which is an additive value for the prediction of drought, it offers a view of the different dimensions such as the spatial distribution, the temporal distribution and the severity detection of this phenomenon. Big data analytic and drought are introduced and reviewed in this paper. Besides, this review includes different studies, researches and applications of big data to drought monitoring. Challenges related to data life cycle such as data challenges, data processing challenges and data infrastructure management challenges are also discussed. Finally, we conclude that big data analytic can be beneficial in drought monitoring but there is a need for statistical and artificial intelligence-based approaches. |
| WOS关键词 | ARTIFICIAL-INTELLIGENCE ; TIME-SERIES ; AGRICULTURAL DROUGHT ; RESPONSE INDEX ; NEURAL-NETWORK ; FORECAST ; MODEL ; EVAPOTRANSPIRATION ; TECHNOLOGIES ; COMBINATION |
| 资助项目 | National Key Research and Development Program of China[2019YFA0606903] ; National Natural Science Foundation of China[41971040] ; Youth Innovation Promotion Association CAS[2017074] ; CAS Interdisciplinary Innovation Team[JCTD-2019-04] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000591930600008 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; CAS Interdisciplinary Innovation Team |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156357]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Balti, Hanen; Sang, Yanfang |
| 作者单位 | 1.Ecole Natl Sci Informat, Lab RIADI, La Manouba 2010, Tunisia 2.Univ Paris 08, Lab Informat Avancee St Denis LIASD, Paris, France 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 4.Univ Sherbrooke, Ctr Applicat & Rech Teledetect CARTEL, Sherbrooke, PQ J1K 2R1, Canada 5.IMT Atlantique, Lab ITI Dept, F-29238 Brest, France |
| 推荐引用方式 GB/T 7714 | Balti, Hanen,Ben Abbes, Ali,Mellouli, Nedra,et al. A review of drought monitoring with big data: Issues, methods, challenges and research directions[J]. ECOLOGICAL INFORMATICS,2020,60:17. |
| APA | Balti, Hanen,Ben Abbes, Ali,Mellouli, Nedra,Farah, Imed Riadh,Sang, Yanfang,&Lamolle, Myriam.(2020).A review of drought monitoring with big data: Issues, methods, challenges and research directions.ECOLOGICAL INFORMATICS,60,17. |
| MLA | Balti, Hanen,et al."A review of drought monitoring with big data: Issues, methods, challenges and research directions".ECOLOGICAL INFORMATICS 60(2020):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。