Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh
This study investigates drought trends, SPI-SPEI comparisons, and predictions in Rangpur, Bangladesh, from 1979 to 2020. We employed Modified Mann-Kendall for trend analysis, SPI and SPEI for drought assessment, and Pearson Correlation Coefficient and Simple Linear Regression for evaluating SPI and...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Geology, Ecology, and Landscapes |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/24749508.2023.2254003 |
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| author | Mst. Labony Akter Md. Naimur Rahman Syed Anowerul Azim Md. Rakib Hasan Rony Md. Salman Sohel Hazem Ghassan Abdo |
| author_facet | Mst. Labony Akter Md. Naimur Rahman Syed Anowerul Azim Md. Rakib Hasan Rony Md. Salman Sohel Hazem Ghassan Abdo |
| author_sort | Mst. Labony Akter |
| collection | DOAJ |
| description | This study investigates drought trends, SPI-SPEI comparisons, and predictions in Rangpur, Bangladesh, from 1979 to 2020. We employed Modified Mann-Kendall for trend analysis, SPI and SPEI for drought assessment, and Pearson Correlation Coefficient and Simple Linear Regression for evaluating SPI and SPEI relationships. Additionally, we utilized ANN, SVM, and RF for prediction. The study revealed notable negative trends in seasonal and annual drought, with the highest z statistics observed for SPI 06 (-2.75), SPI 09 (-4.50), SPI 12 (5.60), SPI 24 (-8.40), SPEI 06 (-5.13), SPEI 09 (-6.82), SPEI 12 (-8.04), and SPEI 24 (-11.20). Strong correlations were identified across all SPI and SPEI indices, with coefficients peaking at 97%, 98%, 98%, and 97% for 06, 09, 12, and 24-month periods, respectively. The comparative assessment favored SPEI over SPI, highlighting its superiority and accuracy. The ANN prediction model showed significant results for short-term and seasonal drought forecasts, projecting SPEI 03 and SPEI 06 increases of 0.02 and 0.24, respectively. However, long-term drought estimation exhibited insignificant performance across all predictive models. This emphasizes the need for developing essential predictive tools for future drought variability. |
| format | Article |
| id | doaj-art-d9c7c90c3d2548e98703e4887cf740c8 |
| institution | Kabale University |
| issn | 2474-9508 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geology, Ecology, and Landscapes |
| spelling | doaj-art-d9c7c90c3d2548e98703e4887cf740c82025-08-20T03:48:24ZengTaylor & Francis GroupGeology, Ecology, and Landscapes2474-95082025-04-019259661010.1080/24749508.2023.2254003Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, BangladeshMst. Labony Akter0Md. Naimur Rahman1Syed Anowerul Azim2Md. Rakib Hasan Rony3Md. Salman Sohel4Hazem Ghassan Abdo5Department of Geography and Environmental Science, Begum Rokeya University, Rangpur, BangladeshCenter for Archaeological Studies, University of Liberal Arts Bangladesh, Dhaka, BangladeshDepartment of Geography and Environmental Science, Begum Rokeya University, Rangpur, BangladeshDepartment of Geography and Environmental Science, Begum Rokeya University, Rangpur, BangladeshDepartment of Development Studies, Daffodil International University, Dhaka, BangladeshGeography Department, Faculty of Arts and Humanities, Tartous University, Tartous, SyriaThis study investigates drought trends, SPI-SPEI comparisons, and predictions in Rangpur, Bangladesh, from 1979 to 2020. We employed Modified Mann-Kendall for trend analysis, SPI and SPEI for drought assessment, and Pearson Correlation Coefficient and Simple Linear Regression for evaluating SPI and SPEI relationships. Additionally, we utilized ANN, SVM, and RF for prediction. The study revealed notable negative trends in seasonal and annual drought, with the highest z statistics observed for SPI 06 (-2.75), SPI 09 (-4.50), SPI 12 (5.60), SPI 24 (-8.40), SPEI 06 (-5.13), SPEI 09 (-6.82), SPEI 12 (-8.04), and SPEI 24 (-11.20). Strong correlations were identified across all SPI and SPEI indices, with coefficients peaking at 97%, 98%, 98%, and 97% for 06, 09, 12, and 24-month periods, respectively. The comparative assessment favored SPEI over SPI, highlighting its superiority and accuracy. The ANN prediction model showed significant results for short-term and seasonal drought forecasts, projecting SPEI 03 and SPEI 06 increases of 0.02 and 0.24, respectively. However, long-term drought estimation exhibited insignificant performance across all predictive models. This emphasizes the need for developing essential predictive tools for future drought variability.https://www.tandfonline.com/doi/10.1080/24749508.2023.2254003DroughtSPISPEImachine learningrangpur |
| spellingShingle | Mst. Labony Akter Md. Naimur Rahman Syed Anowerul Azim Md. Rakib Hasan Rony Md. Salman Sohel Hazem Ghassan Abdo Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh Geology, Ecology, and Landscapes Drought SPI SPEI machine learning rangpur |
| title | Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh |
| title_full | Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh |
| title_fullStr | Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh |
| title_full_unstemmed | Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh |
| title_short | Estimation of drought trends and comparison between SPI and SPEI with prediction using machine learning models in Rangpur, Bangladesh |
| title_sort | estimation of drought trends and comparison between spi and spei with prediction using machine learning models in rangpur bangladesh |
| topic | Drought SPI SPEI machine learning rangpur |
| url | https://www.tandfonline.com/doi/10.1080/24749508.2023.2254003 |
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