A critical review of hurricane risk assessment models and predictive frameworks

Abstract

Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure. Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard, vulnerability, exposure, and mitigation capacity, crucial for understanding and managing the risks hurricanes pose to communities. These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios. A systematic review was conducted, encompassing 94 articles, to scrutinize the structure, data inputs, assumptions, methodologies, perils modelled, and key predictors of hurricane risk. This review identified key research gaps essential for enhancing future risk assessments. The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril, commonly storm surge and flood, resulting in inadequacies in disaster resilience planning. Most risk assessments were based on hurricane frequency rather than hurricane damage, which is more insightful for policymakers. Furthermore, considering secondary indirect impacts stemming from hurricanes, including real estate market and business interruption, could enrich economic impact assessments. Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5% of studies. The top six predictive factors for hurricane risk were land use, slope, precipitation, elevation, population density, and soil texture/drainage. Another notable research gap identified was the potential of machine learning techniques in risk assessments, offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions. Existing machine learning based risk assessments leverage random forest models (42% of studies) followed by neural network models (19% of studies), with further research required to investigate diverse machine learning algorithms such as ensemble models. A further research gap is model validation, in particular assessing transferability to a new study region. Additionally, harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments. By addressing these research gaps, hurricane risk assessments can furnish invaluable insights for national policymakers, facilitating the development of robust hurricane mitigation strategies and the construction of hurricane-resilient communities. To the authors’ knowledge, this represents the first literature review specifically dedicated to quantitative hurricane risk assessments, encompassing a comparison of Multi-criteria Decision Making (MCDM), numerical models, and machine learning models. Ultimately, advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future.