Predicting and communicating flood risk of transport infrastructure based on watershed characteristics

• This research aims to identify and communicate water-related vulnerabilities in transport infrastructure, specifically flood risk of road/rail-stream intersections, based on watershed characteristics. This was done using flooding in Varmland and Vastra Gotaland, Sweden in August 2014 as case studies on which risk models are built. Three different statistical modelling approaches were considered: a partial least square regression, a binomial logistic regression, and artificial neural networks. Using the results of the different modelling approaches together in an ensemble makes it possible to cross-validate their results. To help visualize this and provide a tool for communication with stakeholders (e.g., the Swedish Transport Administration - Trafikverket), a flood 'thermometer' indicating the level of flooding risk at a given point was developed. This tool improved stakeholder interaction and helped highlight the need for better data collection in order to increase the accuracy and generalizability of modelling approaches.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)

NATURVETENSKAP  -- Geovetenskap och miljövetenskap (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences (hsv//eng)

Keyword

Flood prediction

Stakeholder interactions

Transport infrastructure

PLS

Binomial regression

Artificial neural network

Publication and Content Type

ref (subject category)

art (subject category)