Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm
Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situatio...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2024
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| Acceso en línea: | https://hdl.handle.net/10568/163858 |
| _version_ | 1855537882677116928 |
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| author | Al Mamun, Md. Abdullah Sarker, Mou Rani Sarkar, Md. Abdur Rouf Roy, Sujit Kumar Nihad, Sheikh Arafat Islam McKenzie, Andrew M. Hossain, Md. Ismail Kabir, Md. Shahjahan |
| author_browse | Al Mamun, Md. Abdullah Hossain, Md. Ismail Kabir, Md. Shahjahan McKenzie, Andrew M. Nihad, Sheikh Arafat Islam Roy, Sujit Kumar Sarkar, Md. Abdur Rouf Sarker, Mou Rani |
| author_facet | Al Mamun, Md. Abdullah Sarker, Mou Rani Sarkar, Md. Abdur Rouf Roy, Sujit Kumar Nihad, Sheikh Arafat Islam McKenzie, Andrew M. Hossain, Md. Ismail Kabir, Md. Shahjahan |
| author_sort | Al Mamun, Md. Abdullah |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981–2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh. |
| format | Journal Article |
| id | CGSpace163858 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1638582025-05-14T10:24:26Z Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm Al Mamun, Md. Abdullah Sarker, Mou Rani Sarkar, Md. Abdur Rouf Roy, Sujit Kumar Nihad, Sheikh Arafat Islam McKenzie, Andrew M. Hossain, Md. Ismail Kabir, Md. Shahjahan Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981–2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh. 2024-01-04 2024-12-19T12:53:05Z 2024-12-19T12:53:05Z Journal Article https://hdl.handle.net/10568/163858 en Open Access Springer Al Mamun, Md. Abdullah; Sarker, Mou Rani; Sarkar, Md Abdur Rouf; Roy, Sujit Kumar; Nihad, Sheikh Arafat Islam; McKenzie, Andrew M.; Hossain, Md. Ismail and Kabir, Md. Shahjahan. 2024. Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm. Sci Rep, Volume 14, no. 1 |
| spellingShingle | Al Mamun, Md. Abdullah Sarker, Mou Rani Sarkar, Md. Abdur Rouf Roy, Sujit Kumar Nihad, Sheikh Arafat Islam McKenzie, Andrew M. Hossain, Md. Ismail Kabir, Md. Shahjahan Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title | Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title_full | Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title_fullStr | Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title_full_unstemmed | Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title_short | Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm |
| title_sort | identification of influential weather parameters and seasonal drought prediction in bangladesh using machine learning algorithm |
| url | https://hdl.handle.net/10568/163858 |
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