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

Assessing multi-hazard susceptibility and understanding community insights are important for effective disaster risk management; however, limited research has been conducted to study these aspects together. This study uses a data-driven approach to assess multi-hazard susceptibility and community perceptions, aiming to deepen climate change mitigation strategies. We employed a two-stage framework in Eastern Hindukush, Pakistan, which is based on machine learning, remote sensing, geographical information systems, and index-based methods. In the first stage, flood and landslide inventories were generated, and predictive factors were analyzed using logistic regression, resulting in an integrated multi-hazard susceptibility map. In the second stage, a survey of 410 household heads assessed community risk perception, communication, and preparedness, using a structured questionnaire with 28 Likert-scale indicators, and a composite index was calculated. The findings indicate that 25.81 % and 35.43 % of the study area are susceptible to flooding and landslides, respectively, with 15.07 % vulnerable to both hazards concurrently. On the other hand, the community is generally aware of flood and landslide risks; however, there are significant gaps in coping abilities and preparedness, including insufficient insurance coverage and training. Moreover, socioeconomic challenges, such as limited access to information and low trust in local authorities, further complicate disaster preparedness efforts. This study provides a holistic framework for identifying multi-hazard hotspots and assessing community perceptions, facilitating targeted interventions to enhance disaster preparedness and resilience in the region.