El objetivo de esta tesis es predecir la magnitud sísmica en el Perú utilizando técnicas de aprendizaje automático. Para ello, se realizó una búsqueda sistemática de la literatura, identificando las principales variables, técnicas, herramientas y metodologías empleadas en diversos trabajos de investigación relacionados con la predicción de terremotos. Para desarrollar los modelos predictivos, se adoptó la metodología CRISP-DM. Primero, se analizaron 23,678 registros sísmicos recopilados por el Instituto Geofísico del Perú. A partir de estos datos, se identificaron las principales variables sísmicas: magnitud, latitud, longitud y profundidad, que se utilizaron para entrenar los modelos. Los datos fueron divididos en dos conjuntos: el 80% para entrenamiento y el 20% restante para validación. Se implementaron cuatro técnicas de aprendizaje automático para desarrollar los...

Technological development has awakened interest on the part of the scientific community in researching to predict earthquakes. The article's objective is to know what variables, techniques, tools, and methodologies have been used in the different studies to predict earthquakes using machine learning techniques. To carry out the study, the Kitchenham methodology was used, which consists of three development phases: review planning, conducting, and reporting. In the planning phase, four research questions were posed; for this purpose, an exhaustive literature search was carried out. After carrying out the selection and exclusion criteria, the questions posed were developed, of which it was found that 15% of the variables to predict earthquakes were latitude, longitude, and depth. In comparison, 13% were the seismic magnitude. 17% of the most used techniques were Random Forest, followed by ...