New paper published in Applied Ocean Research explores the deep learning models that predict how our coastlines will change, and how certain we can be about those predictions.
Research published in NHESS has found that artificial intelligence can be used to help improve fluvial flood risk assessment.
Research into improving how uncertainty is understood and managed in complex flood risk models provides new insights into which uncertainties matter most, and practical approaches to analysis.
Tharindu Manamperi shares his latest research to help predict shoreline change using deep learning methods
Published research by Jake Grainger explores statistical techniques that better utilise ocean buoy data to help characterise the ocean wavefield.
Luke Jenkins publishes research in ‘Natural Hazards’ that provides evidence for the prevalence of consecutive storms, big waves or high tide events occurring over a short period of time (known as clustering) around the UK.
Freya Muir publishes her research in ‘Earth Surface Processes and Landforms’ as well as an open-source tool for mapping coastal change, known as VedgeSat.
Two papers published in the Journal of Hydrology explore how to quantify the impacts of natural flood management (NFM), specifically leaky dams in upland catchments, on the downstream flood peak magnitude.
New research explores events in northern England where river water levels rise very rapidly and are extremely hazardous to river users.
The flood hydrology roadmap sets out a vision to help scientists and practitioners across the UK better predict future flood events and improve flood resilience across the UK.
