Abstract
Climate variability is vital for effective climate adaptation and risk management. This study investigates the temperature variations during the Boro season in Bangladesh and evaluates the performance of multiple machine learning models for predicting both maximum and minimum temperatures across diverse ecosystems. In this study, we employed several machines learning models and the model performance was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results revealed that CatBoost model consistently outperformed other models for Barind and Haor and SVM outperformed for Coastal region, achieving the lowest error metrics across both maximum and minimum temperature predictions. However, The Diebold–Mariano test revealed that linear, DT, and KNN models performed similarly but significantly worse than advanced algorithms, while ensemble methods (RF, GBM, XGBoost, CatBoost) showed no significant differences, indicating robust performance; neural models (CNN, LSTM) yielded mixed results, sometimes aligning with ensembles and sometimes differing significantly. Spatial analysis identified high-risk areas with extreme temperature conditions, particularly in regions like Rajshahi, Natore, and Pabna. These findings emphasize the need for region-specific temperature prediction models and targeted climate adaptation strategies in Bangladesh’s diverse ecosystems. The results highlight the importance of localized predictive models and the need for targeted climate adaptation strategies in the face of temperature extremes across Bangladesh’s diverse ecosystems.
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Data availability
The data supporting the findings of this study are available upon reasonable request from the corresponding author.
Code availability
All analyses were conducted using R (version 4.3.1) and Python (version 3.13), with standard machine learning libraries. The modeling workflow, including preprocessing, imputation, cross-validation, and evaluation, is fully described in the Methods section. The data were obtained from the Bangladesh Meteorological Department (BMD) and are subject to access restrictions; analysis code is available from the corresponding author upon reasonable request.
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Contributions
Niaz Md. Farhat Rahman (NMFR): Conceived and designed the study, conducted statistical analyses, prepared visualizations, and drafted the full manuscript.Nazmul Haque (NH): conducted data analysis, prepared the figures, and drafted the manuscript.Md. Asadullah (MA): Curated and formatted datasets for analysis and contributed to the statistical analysis process.Abul Bashar Md. Zahid Hossain (ABMZH): Contributed to manuscript review and critical revisions.Md. Sabbir Hossain (MSH): Contributed to writing some parts of the manuscript.Md. Jamal Uddin (MJU): Provided guidance on statistical modeling and analysis and contributed to manuscript refinement.S.M. Quamrul Hassan (MBR): Compiled and organized all relevant datasets.Md. Azizul Baten (MAB): Supervised the research project and provided overall guidance throughout the study.
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Rahman, N.M.F., Haque, N., Asadullah, M. et al. Machine learning-based temperature prediction across diverse ecosystems for the Boro Season in Bangladesh.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-46341-z
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DOI: https://doi.org/10.1038/s41598-026-46341-z
Keywords
- Minimum temperature
- Maximum temperature
- Machine learning
- Boro season
- Climate change
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