Spatiotemporal trends and machine learning-based prediction of temperature variability during the T. Aman rice-growing season in Bangladesh.

ABSTRACT: Climate change poses significant risks to food security, especially in agriculture-dependent countries like Bangladesh. This study analyzes temperature trends from 1961 to 2023 using data from the Bangladesh Meteorological Department (BMD) across three climatic regions: Barind, Coastal, and Haor. The Mann-Kendall test revealed statistically significant warming trends in both maximum and minimum temperatures, with the most pronounced increase in the Haor region. Moran's analysis detected clear spatial clustering of high-risk zones, with Barind districts facing severe maximum temperature risks (> 40 °C) and Sylhet showing heightened minimum temperature risks. The MLP model achieved the lowest errors across ecosystems, with MSEs of 0.82 (Barind), 1.47 (Coastal), and 1.50 (Haor) for maximum te

SUBMITTER: Rahman NMF 

PROVIDER: S-EPMC12749183 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature