Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh
Amrin Binte Ahmed, Md. Mahin Uddin Qureshi, Mohammad Mahboob Hussain Khan, Adisha Dulmini, Mohammad Ashraful Haque Mollah, Rumana Rois, Dost Muhammad Khan, Anurag Barthwal, Anurag Barthwal
TL;DR
This study uses hybrid machine learning models to forecast thermal discomfort in Rajshahi, Bangladesh, showing rising heat stress and the need for climate adaptation.
Contribution
The novel use of STL-adjusted hybrid models combining traditional and machine learning methods for forecasting thermal discomfort in a heat-prone region.
Findings
The STL-TBATS-LSTM hybrid model outperformed others in forecasting thermal discomfort with low MAE and RMSE values.
Discomfort peaks from June to August, with increasing frequency and intensity of high-discomfort days from 1985 to 2024.
Forecasts predict 39.8% of days in 2025–2027 will be 'High Discomfort' and 1.2% 'Severe Discomfort'.
Abstract
Extreme heat and humidity pose an increasing threat to human health, labor productivity, and overall well-being, particularly in heat-vulnerable and rapidly urbanizing regions such as Rajshahi, Bangladesh. As rising temperatures and elevated humidity levels intensify exposure to heat stress, accurate forecasting has become essential for effective early warning systems and climate-resilient urban planning. However, modeling thermal discomfort is challenging due to the need to analyze long-term, high-frequency meteorological time series data with complex seasonal and nonlinear structures. Therefore, this study applies and evaluates seasonal-adjusted, machine-learning-based hybrid models to forecast thermal discomfort using 40 years (1985–2024) of daily temperature and humidity data. The Thom’s Discomfort Index (DI), a standard measure of thermal stress, was calculated and decomposed using…
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Taxonomy
TopicsUrban Heat Island Mitigation · Climate Change and Health Impacts · Meteorological Phenomena and Simulations