The Quaternary Active Faults Database of Iberia (QAFI) is an initiative lead by the Institute of Geology and Mines of Spain (IGME) for building a public repository of scientific data regarding faults having documented activity during the last 2.59 Ma (Quaternary). QAFI also addresses a need to transfer geologic knowledge to practitioners of seismic hazard and risk in Iberia by identifying and characterizing seismogenic fault-sources. QAFI is populated by the information freely provided by more than 40 Earth science researchers, storing to date a total of 262 records. In this article we describe the development and evolution of the database, as well as its internal architecture. Additionally, a first global analysis of the data is provided with a special focus on length and slip-rate fault parameters. Finally, the database completeness and the internal consistency of the data are discussed. Even though QAFI v.2.0 is the most current resource for calculating fault-related seismic hazard in Iberia, the database is still incomplete and requires further review. La Base de Datos de Fallas Activas de Iberia (QAFI) es una iniciativa promovida por el Instituto Geológico y Minero de España (IGME) para construir un repositorio público de información científica sobre fallas con actividad en los últimos 2,59 Ma (Cuaternario). Además, la QAFI persigue establecer una base sobre la que facilitar la transferencia de conocimiento geológico al ámbito tecnológico de la gestión del riesgo sísmico en Iberia, en particular en la identificación y caracterización de fuentes sismogénicas tipo falla. La QAFI se ha construido a partir de la información proporcionada de modo altruista por más de 40 investigadores en ciencias de la Tierra conteniendo actualmente un total de de 262 registros. En este artículo se describe la concepción y evolución de la base de datos, y su arquitectura interna. Además, se ofrece un primer análisis global de los datos que contiene, con especial interés en parámetros tan importantes como la longitud y tasa de deslizamiento de las fallas. Finalmente se discuten dos temas cruciales en cualquier base de datos: su completitud y la homogeneidad de los datos. Se concluye que QAFI v.2.0, pese a ser la fuente más actualizada de información disponible en Iberia sobre peligrosidad sísmica de fallas concretas, dista aun de ser completa, por lo que nuevas revisiones y versiones deberán seguir llevándose a cabo en el futuro.

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.