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Item type:Publication, Spatial analysis for water supply in seismic emergencies: The Lima–Callao metropolitan area(Frontiers Media SA, 2024-01-01)In urban areas exposed to high-magnitude earthquakes, the drinking water supply would be severely damaged, and domestic services would be disrupted for a large part of the population in the event of an earthquake. The Lima-Callao metropolitan area in Peru, South America, is expected to experience an 8.8 Mw earthquake, and it is estimated that approximately 90% of the population would not have immediate access to emergency water in the case of such an event. The main objective of this paper is to define criteria for a spatial analysis method to guide the design criteria for an Emergency Water Supply System (EWaSS). Methods: This paper combines territorial, urban resilience and participatory approaches and presents the results of an interdisciplinary research with social impact. Thus, it examines the urban territory at macro-, meso- and micro scales; physical-spatial variables indicating risk levels and possible public spaces to implement the system; and socio-spatial variables regarding the population, risk perception and participation in management to strengthen urban resilience. Normative tools and the Geographic Information System are used to spatialize and systematize quantitative and qualitative information. Results and discussion: The EWaSS is an alternative for safe water supply in a post-disaster situation that would provide immediate and autonomous operation during the first 72 h of the emergency. The results show the physical-spatial and social viability of urbanized areas and the system design criteria that guide local actors in making decisions at the three levels of emergency management. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The More You Know, the Less You Trust. Knowledge Paradox in AI-Driven Disaster Management(Institute of Electrical and Electronics Engineers Inc., 2026-01-01)The effectiveness of AI-driven decision-support systems in disaster management depends not only on technical performance but also on whether end users trust and act upon system outputs, a constraint that engineering design alone cannot resolve. A technically superior flood prediction system that citizens choose to ignore during an emergency offers no practical advantage over no system at all. This study examines sociodemographic, cognitive, and dispositional factors that shape public trust in AI-based disaster management systems, with implications for human-centered system design. Using hierarchical ordinal logistic regression applied to survey data from 272 respondents across Peru and Chile (two countries with high exposure to climate-related hazards), we identify a knowledge–trust paradox with direct relevance to deployment strategy: domain knowledge about disasters significantly reduces trust in AI recommendations (? = ?0.79). At the same time, familiarity with AI systems significantly increases trust (? = +0.84). Gender (? = +2.11), technological optimism (? = +1.44), and income (? = +0.25) emerge as additional significant predictors. Perceived explainability positively predicts trust, supporting the case for transparent, interpretable outputs as a baseline design requirement rather than an optional feature. These findings suggest that AI systems deployed in life-critical scenarios must account for differences in user knowledge profiles: interfaces designed for domain experts require transparency regarding data integration and uncertainty handling. Broader deployment strategies must also address socioeconomic barriers to access and adoption. The results extend algorithm aversion theory to high-stakes emergency contexts and offer actionable guidance for designing trustworthy, inclusive AI systems for disaster response.