Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather
Meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data.
| Autor principal: | |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/122616 |
| _version_ | 1855514894228520960 |
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| author | CGIAR Research Program on Wheat |
| author_browse | CGIAR Research Program on Wheat |
| author_facet | CGIAR Research Program on Wheat |
| author_sort | CGIAR Research Program on Wheat |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. |
| format | Informe técnico |
| id | CGSpace122616 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| record_format | dspace |
| spelling | CGSpace1226162023-03-14T11:42:05Z Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather CGIAR Research Program on Wheat models drought remote sensing development rural development data learning systems weather agrifood systems approaches solutions satellite Meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. 2020-12-31 2022-10-06T14:02:52Z 2022-10-06T14:02:52Z Report https://hdl.handle.net/10568/122616 en Open Access application/pdf CGIAR Research Program on Wheat. 2020. Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather. Reported in Wheat Annual Report 2020. Innovations. |
| spellingShingle | models drought remote sensing development rural development data learning systems weather agrifood systems approaches solutions satellite CGIAR Research Program on Wheat Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title | Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title_full | Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title_fullStr | Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title_full_unstemmed | Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title_short | Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| title_sort | set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather |
| topic | models drought remote sensing development rural development data learning systems weather agrifood systems approaches solutions satellite |
| url | https://hdl.handle.net/10568/122616 |
| work_keys_str_mv | AT cgiarresearchprogramonwheat setofsolutionsusingremotesensingandsupervisedlearningtoreplaceautoregressiveintegratedmovingaveragemodelstoforecastweather |