Resumen
In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays a critical role in ensuring the safety and operational efficiency of facilities. This case study uses a combination of the multi-criteria analysis approach and hydrological studies that use machine learning algorithms to simulate new rainfall events in order to estimate the risk of flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, and land use and land cover, will be weighed using the multicriteria approach. A methodical evaluation of the most vulnerable locations on the railroad network will be possible thanks to the analysis of these parameters based on the geographic information system (GIS) approach. In the meantime, historical precipitation, flow, and hydrological balance data will be used to calibrate and validate hydrological models. The database required for the machine learning model can be created with these hydrological data. The research regions are situated in the densely rail-networked state of Minas Gerais. The geographical and climatic diversity of Minas Gerais makes it the perfect place to test and validate the suggested approaches. The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R2 value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011).