Data-driven multi-hazard susceptibility and community perceptions assessment using a mixed-methods approach.

Area of Science:

• Environmental Science
• Disaster Management
• Geospatial Analysis

Background:

• Effective disaster risk management requires integrating multi-hazard susceptibility assessments with community insights.
• Limited research has jointly addressed multi-hazard risks and community perceptions for climate change adaptation.
• The Eastern Hindukush region in Pakistan faces significant multi-hazard threats, necessitating localized risk assessment.

Purpose of the Study:

• To develop and apply a data-driven framework for assessing integrated multi-hazard susceptibility (floods and landslides).
• To evaluate community risk perception, communication, and preparedness in relation to these hazards.
• To provide insights for enhancing disaster risk reduction strategies and climate change mitigation.

Main Methods:

• A two-stage framework combining machine learning, remote sensing, GIS, and index-based methods.
• Stage 1: Generation of flood and landslide inventories, logistic regression analysis for susceptibility mapping.
• Stage 2: Household surveys (n=410) using structured questionnaires to assess community risk perception and preparedness.

Main Results:

• 25.81% and 35.43% of the study area are susceptible to floods and landslides, respectively.
• 15.07% of the area faces concurrent susceptibility to both floods and landslides.
• Community awareness of risks is high, but coping abilities and preparedness (insurance, training) are insufficient, compounded by socioeconomic challenges.

Conclusions:

• The study presents a holistic framework for identifying multi-hazard hotspots and understanding community vulnerabilities.
• Findings underscore the need for targeted interventions to bridge the gap between risk awareness and preparedness.
• Integrating geospatial analysis with socio-economic data is crucial for effective disaster resilience building in hazard-prone regions.