A predictive model based on machine learning to estimate fllod and landsline risk vulnerabilities case study: Educational Institutions of Peru

Abstract

The El Niño phenomenon is a natural phenomenon that happens every year in Peruvian territory. It brings with it problems such as torrential rains that cause floods. Many educational institutions are built in the Peruvian territory without being part of a study of soils or vulnerabilities such as floods or landslides, perhaps due to the study's cost since they have to respect governmental technical standards required for the construction of an educational entity. Given this, in the present work, the authors propose a predictive model based on machine learning to estimate vulnerabilities from the data of the area of a public institution. Using Machine Learning, the model has been trained using various algorithms and data from a dataset with more than 65 thousand records published by the Ministry of Education of Peru.