Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh
This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition...
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| Language: | English |
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Technoscience Publications
2024-12-01
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| Series: | Nature Environment and Pollution Technology |
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| Online Access: | https://neptjournal.com/upload-images/(24)D-1659.pdf |
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| author | Al Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul Islam |
| author_facet | Al Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul Islam |
| author_sort | Al Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul Islam |
| collection | DOAJ |
| description | This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns. |
| format | Article |
| id | doaj-art-012bd63fdd4844358c219baa27e221d0 |
| institution | Kabale University |
| issn | 0972-6268 2395-3454 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Technoscience Publications |
| record_format | Article |
| series | Nature Environment and Pollution Technology |
| spelling | doaj-art-012bd63fdd4844358c219baa27e221d02025-01-20T07:13:36ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542024-12-012342201220910.46488/NEPT.2024.v23i04.024Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in BangladeshAl Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul IslamThis work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.https://neptjournal.com/upload-images/(24)D-1659.pdfmarkov chain, rainfall forecasting, stationary test statistic, climate, bangladesh |
| spellingShingle | Al Mamun Pranto, Usama Ibn Aziz, Lipon Chandra Das, Sanjib Ghosh and Anisul Islam Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh Nature Environment and Pollution Technology markov chain, rainfall forecasting, stationary test statistic, climate, bangladesh |
| title | Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh |
| title_full | Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh |
| title_fullStr | Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh |
| title_full_unstemmed | Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh |
| title_short | Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh |
| title_sort | forecasting precipitation using a markov chain model in the coastal region in bangladesh |
| topic | markov chain, rainfall forecasting, stationary test statistic, climate, bangladesh |
| url | https://neptjournal.com/upload-images/(24)D-1659.pdf |
| work_keys_str_mv | AT almamunprantousamaibnazizliponchandradassanjibghoshandanisulislam forecastingprecipitationusingamarkovchainmodelinthecoastalregioninbangladesh |