Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures.

This study explores the application of the Rain-on-Grid approach within the two-dimensional (2D) Hydrologic Engineering Center’s River Analysis System (HEC-RAS, version 6.6) for a selected area (~ 230 km²) of the Low Calore River catchment in Southern Italy, which was heavily hit by an extreme rainfall event on October 14–15, 2015. This event, lasting about 17 h, triggered a range of geo-hydraulic phenomena, including extensive flooding of the Calore River, with physical damage to the railway infrastructure. The hydrodynamic model was used to reconstruct the effects of the observed rainfall event by including relevant processes such as spatially distributed rainfall, upstream discharge input, infiltration losses, and flow propagation across hillslopes and within the Calore River. The results demonstrate that 2D Rain-on-Grid HEC-RAS simulations, which account for the minor tributary network, can reproduce realistic correlations with recorded damage to linear transport infrastructure. The comparison with a traditional fluvial flooding approach, where a given discharge hydrograph is used as the only input to the hydrodynamic model, shows that the traditional approach fails to evaluate the activation of the minor tributary network, leading to an underestimation of potential infrastructure damages, and the inability to explain observed damages. These results suggest that hazard maps should explicitly model pluvial and compound pluvial-fluvial flooding when assessing risks to transportation networks. However, the findings also highlight certain limitations, including the need for more detailed and spatially distributed input data and increased computational time.

Climate change and urbanization, among various factors, are expected to exacerbate the risk of flood disasters in urban areas. This prompts the construction of appropriate modeling tools capable of addressing full-scale urban floods for hazard and risk assessment. In this view, sub-grid porosity models based on the classic shallow water equations (SWE) appear to be a promising approach for full-scale applications in urban environments with reduced computational cost with respect to classic SWE models on high-resolution grids. The present work focuses on the recently proposed two-dimensional binary single porosity (BSP) model, which is a porosity flooding model written in differential form and based on the use of a binary indicator function to locate obstacles and buildings. Several applications (synthetic, experimental, and real-world cases) show that (i) the BSP results tend to the classic SWE solution for sufficiently refined mesh and that (ii) the BSP model can be successfully applied to realistic conditions with complicated terrain and obstacle distribution on coarser grids. Clearly, the adoption of medium/coarse grids makes the BSP model inherently less accurate than the classic SWE model on high-resolution grids, but the corresponding reduction of computational cost makes the use of the BSP model promising in full-scale urban flood applications when (i) multiple simulations are needed to perform stochastic or scenario analysis, (ii) no detailed information of local flow characteristics is required, and/or (iii) for complementing classic SWE models in a nesting cascade.

Complete sub-hourly rainfall datasets are critical for accurate flood modeling, real-time forecasting, and understanding of short-duration rainfall extremes. However, these datasets often contain missing values due to sensor or transmission failures. Recovering missing values (or filling these data gaps) at high temporal resolution is challenging due to the imbalance between rain and no-rain periods. In this study, we developed and tested two approaches for the imputation of missing 10-min rainfall data by means of machine learning (Multilayer Perceptron and Random Forest) and interpolation methods (Inverse Distance Weighting and Ordinary Kriging). The (a) direct approach operates on raw data to directly feed the imputation models, while the (b) two-step approach first classifies time steps as rain or no-rain with a Random Forest classifier and subsequently applies an imputation model to predicted rainfall depth instances classified as rain. Each approach was tested under three spatial scenarios: using all nearby stations, using stations within the same cluster, and using the three most highly correlated stations. An additional test involved the comparison of the results obtained using data from the imputed time interval only and data from a time window containing several time intervals before and after the imputed time interval. The methods were evaluated with reference to two different environments, mountainous and coastal, in Campania region (Southern Italy), under data-scarce conditions where rainfall depth is the only available variable. With reference to the application of the two-step approach, the Random Forest classifier shows a good performance both in the mountainous and in the coastal area, with an average weighted F1 score of 0.961 and 0.957, and an average Accuracy of 0.928 and 0.946, respectively. The highest performance in the regression step is obtained by the Random Forest in the mountainous area with an R² of 0.541 and an RMSE of 0.109 mm, considering a spatial configuration including all stations. The comparison with the direct approach results shows that the two-step approach consistently improves accuracy across all scenarios, highlighting the benefits gained from breaking the data imputation process in stages where different physical conditions (in this case, rain and no-rain) are separately managed. Another important finding is that the use of time windows containing data lagged with respect to the imputed time interval allows capturing the atmospheric dynamics by connecting rainfall instances at different time levels and distant stations. Finally, the study confirms that machine learning models outperform spatial interpolation methods, thanks to their ability to manage data with complicated internal structure.