Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais
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 stud...
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MDPI AG
2025-01-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/1/12 |
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| author | Fernanda Oliveira de Sousa Victor Andre Ariza Flores Christhian Santana Cunha Sandra Oda Hostilio Xavier Ratton Neto |
| author_facet | Fernanda Oliveira de Sousa Victor Andre Ariza Flores Christhian Santana Cunha Sandra Oda Hostilio Xavier Ratton Neto |
| author_sort | Fernanda Oliveira de Sousa |
| collection | DOAJ |
| description | 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 R<sup>2</sup> value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011). |
| format | Article |
| id | doaj-art-067189dfbab444729d265cba9c986c51 |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-067189dfbab444729d265cba9c986c512025-01-24T13:35:24ZengMDPI AGInfrastructures2412-38112025-01-011011210.3390/infrastructures10010012Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas GeraisFernanda Oliveira de Sousa0Victor Andre Ariza Flores1Christhian Santana Cunha2Sandra Oda3Hostilio Xavier Ratton Neto4Transport Engineering Program, PET/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-598, BrazilEscuela de Construcción Civil, Pontificia Universidad Católica de Chile, Santiago de Chile 782-0436, ChilePrograma de Pós Graduação em Sensoriamento Remoto (PPGSR), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90501-90, BrazilTransport Engineering Program, PET/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-598, BrazilTransport Engineering Program, PET/COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-598, BrazilIn 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 R<sup>2</sup> value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011).https://www.mdpi.com/2412-3811/10/1/12flood risk assessmentmachine learninghierarchical multi-criteria analysisinfrastructure resiliencehydrological modeling |
| spellingShingle | Fernanda Oliveira de Sousa Victor Andre Ariza Flores Christhian Santana Cunha Sandra Oda Hostilio Xavier Ratton Neto Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais Infrastructures flood risk assessment machine learning hierarchical multi-criteria analysis infrastructure resilience hydrological modeling |
| title | Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais |
| title_full | Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais |
| title_fullStr | Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais |
| title_full_unstemmed | Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais |
| title_short | Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais |
| title_sort | multi criteria assessment of flood risk on railroads using a machine learning approach a case study of railroads in minas gerais |
| topic | flood risk assessment machine learning hierarchical multi-criteria analysis infrastructure resilience hydrological modeling |
| url | https://www.mdpi.com/2412-3811/10/1/12 |
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