Flood risk assessment for road infrastructures using Bayesian networks: case study of Santarem - Portugal

Abstract

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available.

The Determination of Development Priorities Road Infrastructure at “Dinas Pekerjaaan Umum dan Penataan Ruang Kabupaten Balangan” Using AHP and Bayes Methods

“…The study found that 61% of sub-basins in Santarem have a low flood risk, 30% have a medium flood risk, and 9% have a high flood risk. The most influential factors on high flood risk were identified as the EPSI factor, light traffic vehicles, and soil type (Arango, Santamaria, Nogal, Sousa, & Matos, 2022).…”