Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia
The overarching objective of this study is to address this problem through the development of a drought monitoring and forecasting system, leveraging the synergistic use of Principal Component Analysis (PCA) and convolutional long short term memory (ConvLSTM) over Zambia. First, the relationships be...
| Main Authors: | , , |
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| Format: | Informe técnico |
| Language: | Inglés |
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CGIAR System Organization
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/127620 |
| _version_ | 1855522318640480256 |
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| author | Sleimi, Rim Ghosh, Surajit Amarnath, Giriraj |
| author_browse | Amarnath, Giriraj Ghosh, Surajit Sleimi, Rim |
| author_facet | Sleimi, Rim Ghosh, Surajit Amarnath, Giriraj |
| author_sort | Sleimi, Rim |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The overarching objective of this study is to address this problem through the development of a drought monitoring and forecasting system, leveraging the synergistic use of Principal Component Analysis (PCA) and convolutional long short term memory (ConvLSTM) over Zambia. First, the relationships between drought factors (precipitation, temperature, vegetation, soil moisture, and evapotranspiration) were integrated using PCA, and a new cloud-based Multisource Drought Index (CMDI) was constructed. Then, the Spatio-temporal prediction of CMDI on a short-term scale (monthly) was developed using ConvLSTM. The effectiveness of the CMDI in monitoring drought in Zambia was verified by SPI-1 12 based on the IMERG dataset; gross primary production (GPP), and other remote sensing indices that have been used for drought monitoring. The results show that CMDI is well correlated with the SPI and GPP. |
| format | Informe técnico |
| id | CGSpace127620 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | CGIAR System Organization |
| publisherStr | CGIAR System Organization |
| record_format | dspace |
| spelling | CGSpace1276202025-03-11T09:50:20Z Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia Sleimi, Rim Ghosh, Surajit Amarnath, Giriraj climate change agriculture forecast drought water The overarching objective of this study is to address this problem through the development of a drought monitoring and forecasting system, leveraging the synergistic use of Principal Component Analysis (PCA) and convolutional long short term memory (ConvLSTM) over Zambia. First, the relationships between drought factors (precipitation, temperature, vegetation, soil moisture, and evapotranspiration) were integrated using PCA, and a new cloud-based Multisource Drought Index (CMDI) was constructed. Then, the Spatio-temporal prediction of CMDI on a short-term scale (monthly) was developed using ConvLSTM. The effectiveness of the CMDI in monitoring drought in Zambia was verified by SPI-1 12 based on the IMERG dataset; gross primary production (GPP), and other remote sensing indices that have been used for drought monitoring. The results show that CMDI is well correlated with the SPI and GPP. 2022-12-05 2023-01-19T19:12:54Z 2023-01-19T19:12:54Z Report https://hdl.handle.net/10568/127620 en https://hdl.handle.net/10568/121965 Open Access application/pdf CGIAR System Organization Sleimi R, Ghosh S, Amarnath G. 2022. Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia. CGIAR Climate Resilience Initiative. |
| spellingShingle | climate change agriculture forecast drought water Sleimi, Rim Ghosh, Surajit Amarnath, Giriraj Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title | Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title_full | Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title_fullStr | Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title_full_unstemmed | Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title_short | Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia |
| title_sort | development of drought indicators using machine learning algorithm a case study of zambia |
| topic | climate change agriculture forecast drought water |
| url | https://hdl.handle.net/10568/127620 |
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