Machine learning-based peak flow estimation for improved flood resilience of transportation infrastructure

Accurate and timely prediction of peak flow in streams is essential for transportation safety as these estimates can help transportation authorities implement precautionary measures (e.g., road closures, diversion, emergency routes, transportation planning, flood impact assessment, etc.) well ahead of time to mitigate the impacts of flooding on transportation. Often in practice, flow quantiles are estimated from catchment and climate attributes using simple methods such as linear regression, which overlooks the more complex nature of relationships between variables, potentially leading to errors and uncertainties in the estimates that can trickle down to engineering design. Here, we will discuss findings from our ongoing work on accurate estimation of peak flow using machine learning algorithms. The methodology involves a two-step process. First, k-means clustering is implemented to identify regions that have similarities in the mean annual runoff. Second, Random Forest is implemented to map a wide range of climate and catchment features to flow quantiles in each cluster. To assess the effectiveness of this approach in increasing transportation resilience, we will show how the peak flow estimates from this new approach compare with the estimates from the existing approach followed by the Nebraska Department of Transportation and explore the potential of these new estimates to be used for operational purposes for flood-related decision-making in the context to transportation infrastructure.