Bangladesh is a tropical nation where there are notable seasonal temperature changes. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used in this study to forecast Bangladesh's maximum temperature from 2023 to 2042. The objective is to assess how rising temperatures can affect public health, energy consumption, and agriculture. Autocorrelation and partial autocorrelation analysis will be used to improve the model. Analysis was done using historical maximum temperature data spanning from 1981 to 2022. Forecasts were produced using the SARIMA model, whose parameters were chosen in accordance with plots of the autocorrelation function (ACF) and partial autocorrelation function (PACF). The model SARIMA (1,1,2)(0,0,1) is selected based on AIC. In order to account for forecast uncertainty, forecasts were created for the years 2023–2042. 95% prediction ranges were then calculated. Bangladesh's maximum temperatures are predicted by the SARIMA model to rise gradually, from roughly 33.75°C in 2023 to 34.17°C in 2042. With some degree of uncertainty, the 95% prediction intervals show a steady increasing trend between 33.53°C and 34.51°C. The anticipated increase in the highest temperatures has major consequences for Bangladesh. These results highlight how crucial it is to create adaptation plans and laws in order to lessen the effects of warming temperatures and increase resilience.
Published in | American Journal of Biological and Environmental Statistics (Volume 10, Issue 4) |
DOI | 10.11648/j.ajbes.20241004.11 |
Page(s) | 96-104 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
SARIMA Model, Maximum Temperature Forecast, Climate Change, Bangladesh, Temperature Trends
[1] | V. P. Z. A. P. S. C. C. P. S. B. N. C. Y. C. L. G. M. G. M. H. K. L. E. L. J. M. T. M. T. W. O. Y. R. Y. a. B. Z. Masson-Delmotte, "IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change," Cambridge University Press, Cambridge, United Kingdom and New York, 2021. |
[2] | R. J. H. a. G. Athanasopoulos, Forecasting: Principles and Practice, 2018. |
[3] | S. G. a. P. P. Mujumdar, "Future rainfall scenario over Orissa with GCM projections by statistical downscaling," Current Science Association, vol. 90, no. 3, pp. 396-404, 2006. |
[4] | M. M. H. M. R. &. H. M. I. Rahman, "Temperature Forecasting in Dhaka City Using SARIMA Model," Bangladesh Journal of Meteorology, vol. 21, no. 1, pp. 45-53, 2017. |
[5] | A. R. M. T. I. M. S. &. R. M. S. Islam, "Trends in Extreme Temperature Events in Bangladesh.," Atmospheric Research, 2014. |
[6] | M. S. U. Doulah, "Forecasting Temperatures in Bangladesh: An Application," International Journal of Statistics and Mathematics, vol. 5, no. 1, pp. 108-118, September, 2018. |
[7] | S. K. M. MASHFIQUL HUQ CHOWDHURY, "SEASONAL ARIMA APPROACH FOR MODELING AND FORECASTING TEMPERATURES IN BANGLADESH," Journal of Science and Technology, vol. 7, no. 1,2, pp. 29-43, December 2017. |
[8] | K. R. R. K. K. A. R. G. D. N. R. &. H. J. W. Kumar, "Climate impacts on Indian agriculture," International Journal of Climatology, 2020. |
[9] | S. H. J. S. J. E. L. R. C. V. R. M. Z. X. Peng, "Rice yields decline with higher night temperature from global warming.," Proceedings of the National Academy of Sciences, 2004. |
[10] | J. R. V. J. R. A. M. M. P. F. D. A. &. D. D. Welch, "Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures," Proceedings of the National Academy of Sciences, 2010. |
[11] | F. &. K. M. Akram, "The impact of temperature on electricity demand: Evidence from South Asia," Energy Economics, 2021. |
[12] | A. J. W. R. E. &. H. S. McMichael, "Climate change and human health: Present and future risks," The Lancet, 2006. |
[13] | C. Chatfield, "Time-series forecasting," Chapman & Hall/CRC., 2000. |
[14] | J. H. H. B. B. A. C. A. G. X. H. I... &. W. Christensen, Regional climate projections. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press., 2007. |
APA Style
Azim, N. H. M. A., Parvez, S. M., Taharima, M., Ruman, M. S. A. (2024). SARIMA Model-Based Maximum Temperature Forecasting in Bangladesh: A Data-Driven Evaluation from 1981 to 2024. American Journal of Biological and Environmental Statistics, 10(4), 96-104. https://doi.org/10.11648/j.ajbes.20241004.11
ACS Style
Azim, N. H. M. A.; Parvez, S. M.; Taharima, M.; Ruman, M. S. A. SARIMA Model-Based Maximum Temperature Forecasting in Bangladesh: A Data-Driven Evaluation from 1981 to 2024. Am. J. Biol. Environ. Stat. 2024, 10(4), 96-104. doi: 10.11648/j.ajbes.20241004.11
@article{10.11648/j.ajbes.20241004.11, author = {Nur Hosain Md. Ariful Azim and Sofi Mahmud Parvez and Mumtahin Taharima and Md. Sabbir Ahmed Ruman}, title = {SARIMA Model-Based Maximum Temperature Forecasting in Bangladesh: A Data-Driven Evaluation from 1981 to 2024 }, journal = {American Journal of Biological and Environmental Statistics}, volume = {10}, number = {4}, pages = {96-104}, doi = {10.11648/j.ajbes.20241004.11}, url = {https://doi.org/10.11648/j.ajbes.20241004.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20241004.11}, abstract = {Bangladesh is a tropical nation where there are notable seasonal temperature changes. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used in this study to forecast Bangladesh's maximum temperature from 2023 to 2042. The objective is to assess how rising temperatures can affect public health, energy consumption, and agriculture. Autocorrelation and partial autocorrelation analysis will be used to improve the model. Analysis was done using historical maximum temperature data spanning from 1981 to 2022. Forecasts were produced using the SARIMA model, whose parameters were chosen in accordance with plots of the autocorrelation function (ACF) and partial autocorrelation function (PACF). The model SARIMA (1,1,2)(0,0,1) is selected based on AIC. In order to account for forecast uncertainty, forecasts were created for the years 2023–2042. 95% prediction ranges were then calculated. Bangladesh's maximum temperatures are predicted by the SARIMA model to rise gradually, from roughly 33.75°C in 2023 to 34.17°C in 2042. With some degree of uncertainty, the 95% prediction intervals show a steady increasing trend between 33.53°C and 34.51°C. The anticipated increase in the highest temperatures has major consequences for Bangladesh. These results highlight how crucial it is to create adaptation plans and laws in order to lessen the effects of warming temperatures and increase resilience. }, year = {2024} }
TY - JOUR T1 - SARIMA Model-Based Maximum Temperature Forecasting in Bangladesh: A Data-Driven Evaluation from 1981 to 2024 AU - Nur Hosain Md. Ariful Azim AU - Sofi Mahmud Parvez AU - Mumtahin Taharima AU - Md. Sabbir Ahmed Ruman Y1 - 2024/10/18 PY - 2024 N1 - https://doi.org/10.11648/j.ajbes.20241004.11 DO - 10.11648/j.ajbes.20241004.11 T2 - American Journal of Biological and Environmental Statistics JF - American Journal of Biological and Environmental Statistics JO - American Journal of Biological and Environmental Statistics SP - 96 EP - 104 PB - Science Publishing Group SN - 2471-979X UR - https://doi.org/10.11648/j.ajbes.20241004.11 AB - Bangladesh is a tropical nation where there are notable seasonal temperature changes. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used in this study to forecast Bangladesh's maximum temperature from 2023 to 2042. The objective is to assess how rising temperatures can affect public health, energy consumption, and agriculture. Autocorrelation and partial autocorrelation analysis will be used to improve the model. Analysis was done using historical maximum temperature data spanning from 1981 to 2022. Forecasts were produced using the SARIMA model, whose parameters were chosen in accordance with plots of the autocorrelation function (ACF) and partial autocorrelation function (PACF). The model SARIMA (1,1,2)(0,0,1) is selected based on AIC. In order to account for forecast uncertainty, forecasts were created for the years 2023–2042. 95% prediction ranges were then calculated. Bangladesh's maximum temperatures are predicted by the SARIMA model to rise gradually, from roughly 33.75°C in 2023 to 34.17°C in 2042. With some degree of uncertainty, the 95% prediction intervals show a steady increasing trend between 33.53°C and 34.51°C. The anticipated increase in the highest temperatures has major consequences for Bangladesh. These results highlight how crucial it is to create adaptation plans and laws in order to lessen the effects of warming temperatures and increase resilience. VL - 10 IS - 4 ER -