La ubicación del Perú en una región que experimenta actividad sísmica recurrente subrayó la importancia de comprender la vulnerabilidad sísmica de su población, lo cual se logró mediante la consideración de una variedad de factores físicos y sociales relevantes para comunidades específicas. Por ende, el objetivo primordial de esta investigación fue determinar la integración de estos factores para evaluar el nivel de vulnerabilidad sísmica. Para alcanzar este propósito, se desarrolló una herramienta metodológica que empleó parámetros físicos y sociales, como el material predominante de las viviendas, los niveles de pisos, el grupo etario y los ingresos per cápita. Esta herramienta también permitió identificar factores específicos de la zona de estudio de manera remota, mejorando la precisión y reduciendo posibles errores. La ciudad de Pisco fue seleccionada como l...

Traditionally, seismic vulnerability has been assessed considering, for the most part, infrastructure as the main evaluation component, leaving aside other dimensions of study. This result does not adequately evaluate all the anthropogenic characteristics that make a certain system more susceptible to experiencing economic, patrimonial, and human losses in the face of natural phenomena. In this context, Peru is exposed to many different phenomena of the earth’s internal and external geodynamics (i.e., earthquakes, tsunamis, volcanism, mass movements, heavy rains, drought, etc.), earthquakes being the ones that have caused the most damage and repercussions. It is for the latter, considering that seismic hazards are determined by the geographical conditions of the study area, that the main objective of this review is to study and highlight different perspectives of vulnerability analysis...

As Peru is subject to large seismic movements owing to its geographic condition, determining seismic risk levels is a priority task for designing appropriate management plans. These actions become especially relevant when analyzing Pisco, a Peruvian city which has been heavily affected by various seismic events through the years. Hence, this project aims at estimating the associated seismic risk level and its previous requirements, such as hazard and vulnerability. To this end, a hybrid approach of machine learning (i.e., Random Forest) and hierar-chical analysis (i.e., the Saaty matrix) was used. Risk levels were calculated through a double-entry table that establishes the relation between hazard and vulnerability levels. Results suggest that the city of Pisco exhibits both medium (lower city areas) and high (higher city areas) hazard levels in similar proportion. In addition, the coast...