Generación y simulación de un modelo predictivo para prevenir inundaciones en viviendas aledañas a zonas de riesgo mediante técnicas de inteligencia artificial

Abstract

Floods represent one of the disasters that causes most of human and economic losses worldwide. Therefore, in this project the use of several artificial intelligence techniques are proposed with the aim of predicting the water level in Magdalena River, where millions of inhabitants have their houses. For the development of the project, variables such as: historical data of the water level in different seasons, quarter of the year, rainy season and presence or absence of the El Niño-Southern Oscillation (ENSO) phenomenon were used. The results of the MSE error showed a good performance of the different techniques, being the best one Artificial Neural Networks. However, in addition to having a low error level, the PSO technique offered the possibility to interpret the conditions that can trigger a flood.