Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment efficacy in Ancon Bay, Lima, Peru, by using the Papathoma Tsunami Vulnerability Assessment (PTVA-4) model. For this purpose, a detailed 3D representation of the study area was generated using UAV-based oblique photogrammetry, enabling the extraction of building attributes. Additionally, a high-resolution numerical tsunami simulation was conducted using the TUNAMI-N2 model for a potential worst-case scenario that may affect the Central Peru subduction zone, incorporating topographic and land-use data obtained with UAV-based nadir photogrammetry. The results indicate that the northern region of Ancon Bay exhibits higher relative vulnerability levels due to greater inundation depths and more tsunami-prone building attributes. UAV-based assessments provide a rapid and detailed method for evaluating building vulnerability. These findings indicate that the proposed methodology is a valuable tool for supporting coastal risk planning and disaster preparedness in tsunami-prone areas.

The impact of natural hazards can be effectively mitigated through preliminary assessments that combine geospatial and modeling approaches. Among these, tsunami exposure mapping plays a crucial role in coastal risk evaluation. This study develops a methodological framework for tsunami exposure assessment by integrating numerical simulation and geospatial analysis. Two tsunamigenic seismic scenarios were considered: (i) a historically documented event and (ii) a plausible large-magnitude earthquake off the Pacific coast of Lima, Peru. Tsunami propagation and inundation were simulated using the TUNAMI-N2 model to generate detailed inundation maps that delineate the spatial extent and inundation depth. The temporal evolution of tsunami impact distribution was analyzed using arrival-time maps and synthetic tide gauges, strategically located along the coastline to record wave amplitude variations over time. The population exposed to tsunami hazard was quantified using national census demographic data, while critical buildings were identified from official geospatial databases. This integration provides refined insights into tsunami hazard dynamics and supports evidence-based coastal risk management and urban growth.

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.

El levantamiento de información de edificaciones es un componente clave en la planificación urbana y la gestión del riesgo de desastres. En este estudio, se comparan dos métodos semiautomatizados para la estimación del número de pisos de edificaciones: uno basado en imágenes aéreas capturadas con drones y otro a partir de imágenes 360° obtenidas de Google Street View. Para el método fotogramétrico, se generaron modelos tridimensionales y productos fotogramétricos generados a partir de imágenes aéreas. Los resultados mostraron que la precisión en la estimación del número de pisos alcanzó su mayor valor en edificaciones de un solo piso (92%) y el menor en edificaciones de cinco pisos (66%). En el caso del análisis con imágenes 360°, únicamente usando los modelos de clasificación se obtuvo una precisión de más del 0.80% para todas las clases propuestas, aunque con la limitación que no identifica lotes baldíos ni edificaciones mayores a 5 de manera exacta. Asimismo. se implementaron diversos modelos de aprendizaje automático, los cuales, tras la evaluación del desempeño, el modelo Random Forest obtuvo la mejor precisión con un valor de 0.861.

The availability of tsunami hazard and exposure information is crucial to support effective emergency response in coastal areas. This study presents an automated framework that integrates tsunami numerical simulation, geospatial exposure analysis, and WebGIS-based visualization to generate standardized hazard and exposure reports for decision support. Using a parallel implementation of the TUNAMI-N2 model, a 6-h tsunami simulation for the Peruvian coast can be completed in ~45 min, with hazard and exposure reports automatically published on a WebGIS platform within ~3 min. Application to the historical 1746 Lima tsunami demonstrates the system’s capability to quantify hazard and exposure for operational decision-making.

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones.