Using Machine Learning Approaches to Enhance Heatwave Measurement for Vulnerability Assessment and Timely Management of Heat-related Health Services

Abstract

Climate change is one of the most critical challenges facing Australia and the global community today. Data from the Australian Bureau of Meteorology (BoM) indicates that Australia has been experiencing rising temperatures, particularly since the late 20th century. The frequency, duration, and intensity of heatwaves are projected to continue increasing . Since national records began in 1910, Australia has warmed by an average of 1.47°C (±0.24°C), with the highest official temperature recorded at 50.7 degrees Celsius in Onslow, Western Australia (WA), on January 13, 2022. Furthermore, a recent unprecedented high temperature of +41.6°C was recorded during winter on August 26, 2024, in Yampi Sound, WA. Among all natural disasters in Australia, heatwave (HW) represents a leading silent killer and pose a significant public health threat. However, innovative methods for assessing vulnerability for HW-related health services remain limited.

Keywords

• Machine learning
• climate change
• heatwave measurement