A new systematic review has revealed serious shortcomings in the evaluation of cyclone risk in Australia and worldwide.
A new systematic review has revealed serious shortcomings in the evaluation of cyclone risk in Australia and worldwide. The research, which analysed 94 studies on cyclone risk, warns that existing approaches may be failing to provide a full picture of the dangers communities face.
More than 80 cyclones, typhoons and hurricanes form worldwide each year – with Australia facing some of the most powerful and damaging systems. They threaten lives and wreak havoc on infrastructure and economies.
The research identified six major factors that influence cyclone risk: land use, slope, rainfall, elevation, population density, and soil quality. Incorporating these variables into risk models could improve the accuracy of predictions and lead to better-informed policy decisions.
Lead author, Distinguished Professor Biswajeet Pradhan, Director of the Centre for Advanced Modelling and Geospatial Information Systems at the University of Technology Sydney (UTS), said failing to improve risk assessments could leave communities dangerously exposed.
“Our review shows that risk assessments focus too narrowly on specific hazards, such as storm surges or flooding, rather than on how multiple threats interact. This can leave communities unprepared for the full extent of cyclone-related destruction,” Professor Pradhan said.
“Another key concern is that current assessments prioritise cyclone frequency over actual damage, despite the latter being more useful for policymakers. Only 5% of studies examined the effectiveness of mitigation measures, exposing a blind spot in disaster resilience planning.”
Mitigation measures include actions such as improved building codes, coastal defences, early warning systems, and land use planning, all of which can reduce the impact of cyclones and help protect communities.
The economic impact of cyclones is another area where existing assessments fall short. The study notes that indirect effects – such as disruptions to business operations – are often overlooked, despite their potential to cause long-term financial harm.
The study, A critical review of hurricane risk assessment models and predictive frameworks, was recently published in the journal Geoscience Frontiers. It represents the first comprehensive review of cyclone risk assessments.
It follows another recent study by Professor Pradhan, published in Earth Systems and Environment, on the potential of AI and machine learning based risk assessments for cyclone induced flood damage.
“There is untapped potential to use machine learning in cyclone risk assessments,” Professor Pradhan said. “Integrating AI and machine learning could significantly enhance predictive accuracy and resilience planning.
“While some research has used artificial intelligence – including random forest models and neural networks – there is scope to explore more advanced techniques such as ensemble models, which could enhance accuracy and adaptability across different regions.
“These findings offer crucial insights that could shape future research and policy, ultimately helping Australia and other cyclone-prone regions prepare for the increasing threat of extreme weather events in a changing climate,” he said.