Understanding Social Vulnerability to Natural Hazards Through Machine
Learning and Dynamic Spatiotemporal Analysis
Authors: Wang Donglei 
Published: January 2025

Natural hazards pose significant threats to communities, impacting livelihoods
and property. Socially vulnerable communities are disproportionately affected by
these hazards, highlighting the importance of considering social vulnerability
in hazard risk assessment. Research on multi-hazards risk assessment, and social
vulnerability can provide a comprehensive understanding of hazards and local
vulnerability to support the reduction of disaster suffering overall. This
thesis presents a comprehensive study conducted in Idaho, US, to examine the
regional multi-hazards risk and assess the progression of social vulnerability
metrics in wildfire-affected areas in a case study.

The first part of the research proposes a multi-hazards risk assessment
methodology that integrates social vulnerability analysis and machine learning
models. Five machine learning methods, including Naïve Bayes, K-Nearest
Neighbors, Logistic Regression, Random Forest, and K-Means, are utilized for
hazard mapping and characterization of multi-hazards (Flooding, Wildfires, and
Seismic). Additionally, a composite index tailored to the case-study area is
employed to evaluate social vulnerability.

The second part of the research focuses on the analysis of social vulnerability
trends over a 10-year period in wildfire-affected areas as a case study. By
considering short-term and long-term trends, indicators of social vulnerability
are examined across different levels of wildfires risk. The results indicate a
correlation between high wildfires risk levels and elevated social
vulnerability, highlighting the importance of understanding the interplay
between hazards and vulnerability.

The results indicate that RF model performs best in both hazard-related and
social vulnerability datasets. The most cities at multi-hazards risk account for
34.12% of total studied cities (covering 20.80% land). Additionally, high
multi-hazards level and socially vulnerable cities account for 15.88% (covering
4.92% land). This study generates a multi-hazards risk map which show a wide
variety of spatial patterns and a corresponding understanding of where regional
high hazards potential and vulnerable areas are. Additionally, the case study
reveals that high wildfires risk levels generally follow the highest levels of
social vulnerability. There are cities where high or low social vulnerability
remains persistent over time, even in the absence of wildfire effects. Comparing
regions impacted by wildfires to similar non-wildfire affected areas, the case
study identifies a 2.74% rise in civilian unemployment rates and a 0.77% decline
in households earning over $200,000 annually in the wildfire-affected
regions.

The thesis emphasizes the urgent need for information-based prioritization and
effective policy measures to reduce natural hazards risks. The research
contributes to a better understanding of the distribution of multi-hazards risk
and social vulnerability, which can aid in enhancing natural hazards risk
perception, preparedness, and resilience in Idaho and similar regions. The
findings can inform decision-making processes, support targeted interventions,
and promote proactive measures to mitigate the impacts of multi-hazards and
enhance community resilience.