# A Systematic Review: To Increase Transportation Infrastructure Resilience to Flooding Events
## 3 Results
Flood depth was used as the determiner for flood hazard and demographic, social, infrastructure, and economic characteristics were considered [49]. Table 3 represents the 11 studies related to flood prediction and real-time flood forecasting which is Research category B [20; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. Studies within this category considered historical and current flood threats and/or future scenarios to better predict the flood events. Since having sufficient rainfall and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Study Title:** & **Year:** & **Authors:** & **Country of Study Area:** & **Journal:** \\ \hline \multirow{3}{*}{1} & Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models [50] & 2021 & [PERSON], et al. & Vietnam & _Natural Hazards_ \\ \hline \multirow{3}{*}{2} & Estimating Flood Immutation Depth along the Arterial Road Based on the Rainfall Intensity [51] & 2021 & [PERSON] & Indonesia & _Civil and Environmental Engineering_ \\ \hline \multirow{3}{*}{3} & A network percolation-based contagion model of flood propagation and recession in urban road networks [20] & 2020 & [PERSON], et al. & USA & _Scientific Reports_ \\ \hline \multirow{3}{*}{4} & Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA [52] & 2019 & [PERSON], et al. & USA & _Journal of Marine Science and Engineering_ \\ \hline \multirow{3}{*}{5} & Modeling the Impacts of Sea Level Rise on Storm & \multirow{3}{*}{2018} & [PERSON] and & \multirow{3}{*}{USA} & _Marine Technology_ \\  & Surge Inundation in Flood-Prone Urban Areas of Hampton Roads, Virginia [53] & & [PERSON] & USA & _Sciety Journal_ \\ \hline \multirow{3}{*}{6} & A Case Study for the Application of an Operational Two-Dimensional Real-Time Flooding Forecasting System and Smart Water Level Gauges on Roads in Tainn City, Taiwan [54] & 2018 & [PERSON], et al. & Taiwan & _Water_ \\ \hline \multirow{3}{*}{7} & Impact of Sea-Level Rise on Roadway Flooding in the Hampton Roads Region, Virginia [55] & 2017 & [PERSON], et al. & USA & _Journal of Infrastructure Systems_ \\ \hline \multirow{3}{*}{8} & Estimation of Real-Time Flood Risk on Roads Based on Rainfall Calculated by the Revised Method of Missing Rainfall [56] & 2014 & [PERSON], et al. & Korea & _Sustainability_ \\ \hline \multirow{3}{*}{9} & Spatially distributed flood forecasting in flash flood prone areas: Application to road network supervision in Southern France [57] & 2013 & [PERSON], et al. & France & _Journal of Hydrology_ \\ \hline \multirow{3}{*}{10} & Use of radar rainfall estimates and forecasts to prevent flash flood in real time by using a road inundation warning system [58] & 2012 & [PERSON] & France & _Journal of Hydrology_ \\ \hline \multirow{3}{*}{11} & Vulnerability of Hampton Roads, Virginia to Storm-Surge Flooding and Sea-Level Rise [59] & 2006 & [PERSON], et al. & USA & _Natural Hazards_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: 11 Studies of category B.  
water data would increase the accuracy of the prediction models, there are some related discussions and investigations. [PERSON], et al. (2014) estimated real-time flood risks by investigating historical rainfall and the probability of precipitation in Busan, Korea [56]. [PERSON], et al. (2018) found highly accurate flood forecasts by utilizing a two-dimensional real-time forecasting model with improved water gauges that includes recording and transmission of data. It helps track road inundation in real-time in Tainan City, Taiwan [54]. [PERSON], et al. (2013) utilized spatial and temporal rainfall estimate data where water gauges were not present in the Gard Region, France and utilized this data with the hydro-meteorological forecasting approach [57]. [PERSON], et al.