Developing a seasonal-adjusted machine-learning-based hybrid time‑series model to forecast heatwave warning

Heatwaves pose a significant threat to environmental sustainability and public health, particularly in vulnerable regions and rapidly growing cities. They cause water shortages, stress on plants, and an overall drying out of landscapes, reducing plant growth-the basis of energy production and the food chain. Accurate heatwave forecasting is crucial for early warning systems, public health interventions, and disaster preparedness strategies, reducing heat-related mortality risk through modeling and evaluation of warnings. However, anticipating heatwave warnings requires handling the daily time series data, which is a large-scale and high-frequency time series data. High-frequency time series data forecasting presents unique challenges due to its inherent complexity and characteristics. Therefore, the study proposes two algorithms to develop Machine-Learning (ML)-based hybrid models as well as seasonal adjusted ML-based hybrid models, which can handle large datasets and reveal complex seasonal patterns. The performance of these developed ML-based hybrid models and seasonal adjusted ML-based hybrid models were compared with other traditional time series, Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space (ETS), and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) and ML models, Artificial Neural Network (ANN), Support Vector Regression (SVR), Prophet, Random Forest Regression (RFR), and Long Short-Term Memory (LSTM), to forecast heatwave warnings in Rajshahi, one of Bangladesh's warmest districts, based on 42-year historical daily instances. Our findings indicate that the seasonal adjusted ML-based hybrid model, by integrating the Seasonal-Trend decomposition procedure based on LOESS (STL) approach with different time series and ML models, STL-ARIMA-LSTM, outperformed all other models with MAE (0.8974), MAPE (2.9232), RMSE (1.1794), MASE (0.3814) and ACF1 (0.0026). Hence, our suggested seasonal adjusted ML-based hybrid model, ensures a more accurate forecast and helps to determine the number and days of heatwaves, enabling people to plan ahead and take necessary safety measures before they occur.